#yikes. sorry everybody on mobile

[wwhatisstutorials]

The Last Jedi Rey Vest Tutorial

Alright guys! I’m going to do this tutorial a little bit different from other times. Instead of giving a picture of the pattern, I’m going to be putting the different pieces in throughout the process. Here is what I started with so you can get an idea of what piece is for what, but honestly I changed it a lot throughout making it. You’ll understand why

First here are the materials:

2 yards grey herringbone fabric, Grey Thread, batting or stuffing

My fabric is a little darker and has some metallic yarn in it. I got it for 3 dollars a yard at fabricmart and honestly as a broke college student that is a really nice deal and I knew this was not going to be made into a full-fledged cosplay.

1.       First things first. We are going to be making what will turn out to be a&b in my sketch. I honestly was not sure how to go about this so I started with boxy patterns and refined it as I went. Now you are gonna need four of these pieces (2 mirrored). Figure out your measurements too in a small sketch.

How to figure out the arm hole

2.       I decided to combine a&b into one big piece then add the seam in after. Here is how to do that. I always use chalk when working with patterns because it is always easy to come off. That works to our advantage here. Using the photos for reference, figure out where your seam is going to go. Make sure there is a lot of chalk because then you are going to match it up with the other three pieces and transfer the line to make them all symmetrical pieces. If your fabric is not double sided please understand which side is facing out. This Jacket is going to have an inside piece so anything that is going to be shown you want to make sure there is no seams being shown. Pin and sew. Be sure to only sew about a ¼ inch from the edge.

3.       OKAY the Back Piece. I tried my best to not have the seam going down the center but the way I sewed it, there was no possible way I could have left it out. I also decided to only have one back piece instead of two just out of choice. So Instead of making a duplicate, you just need one. Look at the progression of the photos to help you out. Mostly, its just a lot of fitting to get the pattern the exact way you want it. The fit is very important in this vest because the shape of it is what originally caught my eye while watching the movie. Pin the two front pieces to the back and start figuring out how much needs to be taken in where. Use the front pieces to figure out where to chalk the same extra pattern piece. In the back and left/right seams in the back, you gotta utilize these to get shape. Hopefully the photos help. NOTE: I extended the middle back seam all the way down. I realize That is not pictured.

4.       Okay some minor fixes to the front. I took the extra seams we made in the front in a little bit at the bottom to give it some extra shape. Also, look at the front and cut to what you need for the collar. To make it more true to the original I later adjusted the shape of my front from what is shown in the photo.

5.       Okay so now we are going to put the inside and the outside together separately. When you sew the collar together, fit it to your neck. That would mean sewing the collar together at an angle. Once done to both, pin and sew the whole outside of the pieces. MAKE SURE THE GOOD SIDES ARE FACING TOGETHER AND THE UGLY SEAMS ARE FACING OUT.

6.       Okay now attach the back! Make sure the collar and back are the same length so they can fit together nicely. OKAY NOW TAKE A LOOK AT THE PHOTO. I DID SOMETHING VERY FATALLY WRONG AND I HAD TO UNSTITCH AND RESEW BECAUSE mistakes were indeed made. The back is showing the good side facing out when either all the seams should be facing out or all of the good side. Please pay attention to what you are doing.

7.       Alright time for the bottom half! The bottom half is also shaped very nicely. This is pieces c&d. Measure the front and back of the top half where the bottom is going to be attached. You want the seams to line up so keep in mind the fact that you have to make seams which will make the fabric a little shorter. I made d first then used the sides of that to give me the length of the one side for c. This will also be double lined so double everything. Sew the three pieces together, then do so again. Sew the two full bottoms together like a pillow but leave the top open. IRON!! IRON EVERY SEAM MADE. This is very important and will make EVERYTHING EASIER!!

how to get both sides symmetrical. 

Use edge to get edge for c piece

8.       Alright. Now all seams from the top of the vest that have exposed ends, you need to finish them off to prevent fraying. I used zig zag stitches here because they were not shown. In the future stitches I’m going to be using what’s called a blanket stitch. I’ll explain that later. Once you have all the loose ends stitched, sew the bottom half on. It is very important that everything lines up. Take careful notice to that.

9.       Okay now that everything is basically put together except for the shoulders, use the blanket stitch all the way around including the bottom. This is what the stitch looks like. I put the size of the stitch up to as big as it can get on my machine.

(What the stitching and Rey’s actual fabric looks like)

10.   Okay Now onto the shoulders. I ended up making three and picking the best two because these were some-what difficult. To get the size of the half circle, measure from your neck to how far out you want the shoulders to go. Double that and you have your diameter. My diameter was 11 inches with a 5 ½ inch radius. Sew like a pillow, meaning leave one edge open. To apply stuffing or batting. If using batting, cut the shape of the half circle and add it in. If you use stuffing, its going to be a little more difficult. This is what I used and you have to make sure it is even everywhere and do not make it too thick. Once the filling is in, fold in the hole and sew all the way around the piece. Now add lines as guides for you to sew. Take your time. It will be a little frustrating to get it all even.

11.   Okay now jump back to the main form. Pin the shoulder pieces and determine how much of the shoulder you can remove underneath. Now that the arm hole is cut to size and still has raw ends, apply a zig zag stitch all the way around then fold under and secure with the blanket stitch as the bottom half of the hole will be visible. PLEASE make sure that the arm hole is big enough to be comfortable. These were always my down fall and before I did this I made my arm holes bigger. Okay now pin and sew the shoulder piece on making sure to sew on top of the seam that is already on the shoulder pad.

12.   Alright. Cut any Loose thread and admire your handiwork

If you have any questions, feel free to ask. : )

[spectral-musette]

So, the Avengers: Endgame spoiler ban is lifted, and I’ve had a chance to mull over my responses, so I’m finally going to try to write up some thoughts. I was hoping to have seen it again in the interim, but that didn’t work out, so I’m relying on memory from one viewing – it’s possible I’ve missed or misinterpreted things.

Spoilers to follow, so scroll carefully, Ye on Mobile! Also, sorry about the Long Post (TM), I apparently I had a lot to say.

 Time-wise, for its 3 hour length, the film didn’t feel long to me. It maintained its momentum and nothing felt laggy or tedious, even the big battles.

Time travel-wise… Okay, positive stuff first. I thought that revisiting the settings of earlier films was absolutely delightful and nostalgic. It felt very satisfying to have those call backs to earlier adventures and cameos of old enemies (Crossbones, Pierce, Zola, and, surprise, even Sitwell). The Cap vs. Cap fight was hilarious, and I loved seeing Steve so utterly exasperated with himself (“I can do this all d-“, “YEAH, I know.”). The scene in the 70’s was good, though some of the Tony and Howard stuff rang a little hollow to me. I think that’s mostly because I’ve always had trouble reconciling Dominic Cooper’s Young Howard Stark (who I’m very fond of, especially after Agent Carter) to the older version of Howard we see in various flashbacks. They look, sound, and act nothing alike; my friends and I always joke that Hydra replaced Howard sometime in the 60’s. So while an aged up Dominic Cooper Howard probably would’ve made me emotional, as it was, I was more moved to see 20 seconds of Jarvis than for all the stuff with Tony talking to his dad about fatherhood.

Using the “Quantum Realm” for time travel was… okay…. Insofar as the “science” of the Ant-Man films has absolutely never made any damn sense (and that’s …. fine. They’re funny and joyful, and I enjoy them a lot anyway. I don’t go to Marvel movies for “realistic” science fiction), throwing time travel into the mix felt like it just might as well happen. I guess I understand why they chose to go with the “nothing we do in the past can affect our own timelines” approach, but frankly it’s still giving me a headache. I also understand not over-explaining, but there’s a middle ground there that wasn’t quite achieved for me. I guess, based on the scene with Tilda Swinton (sorry, haven’t seen Dr. Strange and don’t know her character’s name) and Bruce, we’re supposed to assume that every journey to the past (cue Anastasia music) creates or perhaps just shifts the time traveler into an alternate reality that branches from their original reality at that point? And then when they travel back to the time they started from via the quantum realm, they return to their original version of reality. So the actions that they take in the past affect that alternate reality, but not the reality that they came from and return to. That’s the only thing I can figure out that makes sense to me at all, but unfortunately the film didn’t make that especially clear. Maybe seeing it again would clarify? So this is gonna be a big factor in how I feel about Steve’s ending, but I’ll get to that in a moment.

Also, a tangent re: time travel… While Tony (an engineer) and Bruce (a biologist) are both brilliant, this seems a little outside their areas of expertise! You know, wouldn’t it be great if we had a character who was an astrophysicist who could really tackle this type of thing - OH HEY, we do! I realize that there were probably issues with getting Natalie Portman back in a substantial role, but I love Jane Foster a lot and I would’ve loved seeing her work with Tony and Bruce to save the universe with a handful of Pym Particles.

OKAY, there’s an awful lot to cover, so I’m going to break down some of my feelings by character just to try to stay organized.

(First, a disclaimer that I haven’t seen Captain Marvel yet, so while Carol seemed like a great character, I don’t have a lot to say since I don’t really know her yet. That said, this seemed like an adequate introduction to the character and I am interested to know more. We have the problem of “if Fury could’ve called her anytime why didn’t he call her during the Chitauri attack/to fight Ultron/etc.” But all the individual titles that come after the team-ups have that problem a little bit… Where were the Other Avengers in Thor 2 or Iron-Man 3, etc.? Sometimes you just have to accept and move on.)

Briefly:

Nebula and Gamora, Tony, Bruce, Scott, with a quick note about Wanda and a very conspicuous absence

And the heavier stuff regarding:

Thor, Natasha, and Steve (and Sam and Bucky).

Nebula and Gamora:

While the Guardians aren’t really my thing, I did vaguely know that in the original Infinity Gauntlet comic storyline, Nebula takes the gauntlet from Thanos and fixes reality. I understand not following the comics exactly for the sake of surprise and to fit with the changed version of the universe, but it still felt wrong to totally take that away from her. Especially given what Thanos has done to her, personally, it seemed fitting that she was going to be the one defeat him. I’m glad she was still pivotal to the story, but it felt like an extra kick in the teeth that past!Nebula was the catalyst for Thanos catching up with our heroes rather than getting to be the one who saves the universe. And forcing her to kill her past self felt like it should’ve been treated with much more gravity than it finally was.

I’m really glad we “saved” Gamora by bringing the version of her from the past into the current timeline (however that works), but I feel so bad for anyone who’s really invested in Gamora/Peter Quill. It’s so heartbreaking that their entire history never happened as far as she’s concerned, that we’ve not only removed that very key relationship, but her character growth over the past how many years. It is at least hopeful; Peter remembers, and has the chance to woo her again, but that’s still got to sting.

Tony:

So Tony Stark sure did die.

I’m not sure… he really needed to? I mean I don’t think I get the rationale of the Infinity Gauntlet killing/maiming the user. I recall the handwavey line about gamma radiation, but if you don’t immediately die after using it, couldn’t you juuuust, say, use the Reality Stone to be like, “hey what if I wasn’t mortally injured”? Couldn’t somebody ELSE do that? I’m not sure I get that.

So that said, I’m not sure if RDJ was really pushing for “you gotta kill me off” for dramatic effect or just to step out of the franchise? It would’ve been kinda cool to see retired Tony working as Avenger-support, working on suits for Rhodey and future Iron-heroes (Iron Patriot? Iron Heart?), mentoring Peter and other youths, and living his nice life with Pepper and their munchkin.

But what a way to go, huh? Dramatic self-sacrifice saving the the planet(/universe?), and a funeral that almost everybody who’s anybody shows up for.

Bruce:

I’m with Valkyrie that I preferred EITHER version to PermaHulk Bruce. Honestly, the Hulk himself had sort of become an independent character, especially after Ragnarok (my issues with Ragnarok aside). So by Bruce settling into this “I look like the Hulk but I act like Bruce” limbo, are we … essentially killing the Other Guy? I don’t like that. I mean I prefer Bruce obviously, but I’m really uncomfortable with that solution.

Scott:

I really love Scott and he was delightful as always in this film. I’m heartbroken for him that he missed (another) 5 years of Cassie’s life, though. I’m also pretty sad we won’t get to see the little girl who has played Cassie so far in any future films since we’ve aged the character up to a teenager. Also, I would’ve liked to see more of Hope! I loved Scott and Hope’s little moment when Hope calls Steve “Cap” and they trade expressions between Scott going “SEE, HE IS REALLY COOL, RIGHT?” and Hope being like “Yeah, okay”.

Overall I guess the Ant-Fam is sorta tangential to the main MCU Avengers cast, so while it was nice to have everybody play together, briefly, I’m pretty content that we’ll see more of Hope (and Janet!) in future Ant-Man/Wasp titles.

 - Similarly, while T’Challa and the Wakanda fam were definitely underused in Endgame (especially the entirely absent Nakia), Black Panther 2 is happening. It’s disappointing to not get a substantial amount of characters that you like in the big team-up films, but it’s good to know they’ll be returning later.

Wanda:

We are really leaving Wanda in a rough place of having lost her twin brother and her android boyfriend within a pretty short amount of time (that’s rough, buddy). Plus, one of the characters that we’ve seen her have a pretty strong bond with is Steve, and he’s out of the picture too. I’m not sure where we’re going with this character, honestly. Hopefully it’s not continuing to hurt her.

It really seemed conspicuous that nobody so much as mentioned Vision by name in this film. Wanda referred to him indirectly, but that was it. I get that Vision isn’t immediately able to be saved since he didn’t vanish in the Gauntlet event, but, yikes, can anybody besides Wanda please attempt to give a damn about him?

I know sometimes we like to pretend that Age of Ultron didn’t happen to us, but Vision was still an interesting character, and some major plot points of Infinity War focused on the value of Vision as a person. I feel pretty bereft that he’s (apparently) gone beyond recall with so little mourning.

Thor:

*heavy sigh*

Thor’s characterization was….???

Unpopular Opinion: despite its good points, I overall didn’t really like Ragnarok, and Thor already sort of felt out of character to me at that point.

Another Unpopular Opinion: I actually really love The Dark World. Thor’s relationship with Jane, and his characterization of gentleness and humility in that era really were important to me.

And I get that Hemsworth is genuinely good at comedy and probably likes doing it. But Thor has always been a funny character. We just used to be laughing with him instead of at him.

I was so uncomfortable with the way the film framed Thor’s brush with depression and alcoholism. Because Thor has lost so much at this point, he has every reason to struggle. I want to say that Thor wouldn’t have given up, but the same time I can believe that this almost unimaginable weight of loss (Frigga, Odin, Loki, Heimdall, The Warriors Three, Asgard itself) would take some toll. And yet the framing of his scenes treats his grief and despair as cause for humor. We’re expected to laugh about an unkempt beard and a big belly instead of being concerned about the fact that a character that we loved considers himself a failure. And there’s nothing funny about this situation to me. It just made me uncomfortable and sad. Revisiting Thor 2 and having him talk to Frigga was on the better side, but I’m disappointed that we couldn’t save her.

Natasha:

*heavier sigh*

Okay, I think a lot of the problem here is that it’s just really difficult to kill a main character any time other than in the last act (we also saw this problem in Star Wars Rebels, but that’s another can of worms). So because Natasha died at such a midway point in the movie, I still can’t shake the feeling that she’s not really dead. Nothing about it felt final to me. Clint trying to emphasize that, because Red Skull said so, it was impossible to bring her back (it’s freaking RED SKULL, why would we trust him???) just made me think even more that she was definitely coming back. Everything seemed to point to her dramatic reappearance and then it just … didn’t happen. That’s not to say it won’t happen in a future film, though, but it still feels deeply unsatisfying and unceremonious now, and that feeling really was a blow to my overall enjoyment of the film.

It also sat really badly with me that Natasha made this choice not just to save Clint (which I would believe; their friendship is really great and I love seeing Natasha’s extremely profound but non-romantic bonds with Clint and with Steve (though I would’ve preferred Natasha/Clint to Natasha/Bruce)), but because she fundamentally felt less worthy than Clint. I don’t like the idea that Natasha went to her death still feeling such guilt, still feeling like a monster (according to that awful scene in AoU), for the things she did as a very young person under the influence of brainwashing. I don’t like that at all.

I’m also really disappointed that we didn’t pursue Natasha and Bucky’s relationship from the comics in the MCU. Because the idea of two people with very similar emotional wounds coming together to support each other as they heal is just really appealing (#looking for baggage that goes with mine). That throwaway line in Civil War (“at least you could recognize me”) really had me convinced that we were going there. I guess we still could, but there are a lot of “ifs” standing in the way now.

Steve:

Another disclaimer: Steve is absolutely my favorite Avenger, and I ship Steve/Peggy really hard.

Aaand I still felt uncomfortable with the resolution.

Maybe it’s just the difficulty I’ve been having getting my head around the time travel shenanigans.

So a lot of the criticisms I’ve heard/read about Steve going back to the 1940’s to Peggy seems to be functioning under the assumption that Steve is living within the timeline as we know it in MCU canon, staying completely hidden, and just not changing any of the bad things that canonically happen: Bucky becoming the Winter Soldier, Hydra infiltrating SHIELD, etc.

But we’ve been told that time travel doesn’t work that way – that Back To The Future, Doctor Who way – in this universe, right? This brings me back to my Alternate Reality take. So my understanding is that after Steve returns the infinity stones to the points in time that the Avengers yoinked them from, he basically occupies an Alternate Reality for a lifetime (Tilda Swinton’s thing about the branched off timelines being consumed by the ~forces of darkness~ only applies IF the infinity stones aren’t returned, and he took care of that). And he could’ve done anything in that Alternate Reality – married Peggy, saved Bucky from Hydra, prevented any wars and disasters he could. Basically it was Steve’s own personal Happiness AU. And then, (presumably after Peggy’s death), he uses the Pym particles and the Quantum Realm to return to his original reality.

Except, in that case, shouldn’t he have returned on the platform instead of dramatically showing up on that park bench?

So…I’m confused and I don’t like it.

Even from the Alternate Reality take, the situation of that choice is complicated. In choosing to be with Peggy, he’s tearing himself out of the lives of all of his loved ones in his Original Reality – Bucky, Sam, Wanda, (whatever the situation was with Sharon Carter that we absolutely never resolved?), etc.

And we’re not completely sure it was a choice, exactly. It’s possible that in the ongoing work to return the infinity stones, Steve somehow got trapped in the past (don’t know why he would’ve had to go to the 40’s, but I guess he could’ve run out of Pym particles there and had to wait for Hank to invent them to even be able to make the trip back).

Also, narratively speaking, it feels a little like we’re invalidating Peggy’s grief, and her character growth that went on in Agent Carter (even if her happy ending with Steve is going on in an Alternate Reality). I wasn’t totally sold on Peggy and Daniel Sousa yet (though I do like Daniel as a character a lot), but Peggy had a whole lifetime that didn’t involve Steve except as a beloved memory. Where is she in that arc when Time Traveler Steve comes back into her life?

Also, even if it IS an Alternate Reality, there would STILL be a version of Steve frozen in the ice in the 1940’s in that reality. How do we deal with that?

And how do we deal with the fact that Steve isn’t the man that Peggy lost anymore. He still loves her, but he’s changed, he’s lived almost a decade since then. How do they find their footing with each other? I’m sure it isn’t impossible, but it’s interesting, and it’s not addressed at all.

I think that’s what bothers me the most – that this is a whole huge adventure – Steve’s entire LIFE – that we’re shoehorning in at the very end of the movie without showing any of the really interesting bits or answering any of our questions about it. I guess that leaves the situation as a fertile ground for the imagination, and maybe that’s a space that the MCU intends to explore someday? I would absolutely watch the hell out of Steve’s Time Travel Romance with Peggy, somebody take my goddamn money.

Anyway, I’m happy about Sam taking up the Shield as Captain America. Bucky-Cap also could’ve been great, but I feel like, with the place we left Bucky in his recovery, he doesn’t need that responsibility yet. Let him rest. Wherever we’re going with the series featuring Sam and Bucky is going to be really interesting, and maybe we’ll get to the point where Bucky really wants to work towards atonement and is ready to share the burden of the Shield with Sam? I’m looking forward to finding out.

Overall, most of my feelings about the movie were pretty positive. It was a complicated story to tell with a lot of characters, and mostly it was handled pretty well. Some of those threads did fall flat for me, but they didn’t totally invalidate the parts of the movie that worked.

[askthegreatraven]

Your wings are really pretty raven! ( Also mun!!! Turn anonymous on! You might get more asks!!! )

“Ah, thank you so much! Yeah, I don’t mean to brag or anything, but I spend a lot of time on them.. That’s how they’re so neat and shiny!”

—

(GDOSKFHDK I’m so sorry this is like seven days late YIKES anyway!! The computer is currently broken so I’m doing everything on mobile! As soon as I can snatch a computer I’ll be turning anon on! Sorry everybody!)

[stargate365]

[SG-1] 6.04: Frozen

Oooh a snow mobile. Penguins!!

Dead seal??

“It’s something.”

Reasearchers in Antarctica are fucking hardcore mate. The stories I have heard.

Poor Jack. He doesn’t like being cold.

“Bit of a weather freak.” Lol.

That don’t look like no seal… Not a Jaffa either

Janet looks so excited.

Jack is beyond confused.

“Darwin would be crushed.”

Jonas is now baffled… this chick isn’t dead?

I keep waiting for her to blink… holy crap, her pupils responded.

Jonas is so sweet.

Yay! Learning is fun!

It’s gonna be healed, isn’t it… yeup.

Yep, Jonas is gonna be her fave… he’s feeding her.

She’s only learnt one English word lol.

Oooh. Spooky.

Janet has an idea! 💡

Sam just cottoned on💡

Whoops, down goes Francine.

Jonas has a crush… that or he’s having a needgasm about meeting a woman from a different evolutionary chain.

Whelp… we all gonna die from an ancient version of Spanish flu by the sound of it.

Holy crap… she understands him?

Yikes… onset of frostbite?

Aiyana looks disturbingly calm. What is she… is she healing him?

Well now, that’s new…

Oh dude, why would you tell her that… yep, she knocked him out… stubborn girl. Why am I not surprised?

Everybody but Jack is fine now… and I’m pretty sure she pushed herself too far.

Correction, not ancient Spanish flu; Alien/Prehistoric Meningitis.

She’s gonna die… I can feel it. ;w; dammit, Jonas deserves a girlfriend.

“Sorry.”

Jack, don’t be an ass.

Dun dun dun…

[cozcat]

because I’ve not actually listened to it in full, thoughts on the entire Mamma Mia: Here We Go Again soundtrack, because @freetobegrace did it and now I need to get it out of my system

and i’m sorry to everyone on mobile who needs to scroll past this

When I Kissed The Teacher:

I’m so glad they changed the pronouns in this song to make it gayer. Kind of wished they’d followed through on that. Just... make Donna canonically bi, you cowards.

when watching it in the movie the vocals seemed so lacking - it sounds so studio-sung compared to their very energetic dancing. so it works better as a soundtrack.

I Wonder (Departure):

I can see why they cut it, it’s a lovely song but it goes nowhere - it’s nearly five minutes of yeah we get it she’s leaving.

A ninety second excerpt would have been nice as it is quite appropriate, though

Hopefully we get it as a deleted scene like we did The Name of the Game

I like the “my friends are my family” line change. But like...zzzzz.......

(I’m also annoyed that they give Rosie and Tanya so few songs and then cut one of them. like I’m here to stan them give me something to react to.)

One Of Us:

how dare you take this song from Donna. I’d have loved to watch Meryl act the fuck out of it.

the way they shot it was cool though

but also holy shit I can’t take their voices they’re so weak

Waterloo:

why did they cast actual stage actors in roles that barely sing and then do this

backing vocals are good though

and it was ridiculous which was fun

Why Did It Have To Be Me:

probably my favourite of the younger dads’ songs, somehow. it’s better suited to questionable vocal abilities, possibly because they haven’t had to alter the vocal range for it, as it was originally written for a man singing it.

not doing a medley with Happy Hawaii was a wasted opportunity, though: it’s a song about a holiday, work with it!

After all I've had to go through / I'm making no plans (making no plans oh-ooh) / But I, but I believe love gives me a second chance, mmm see! throw it in, nobody will even notice except for die-hard ABBA fans losing our goddamned minds!

I've got a feeling the dream will come true / Somebody's waiting and I'll forget you / Hey Honolulu, we're going to happy Hawaii look change the last line back to “why did it have to me" and you’ve got a perfect song end

for context Happy Hawaii has the exact same tune

I Have A Dream:

pretty forgettable... but pretty. worked in context.

seriously it sounds so similar to the version in the original movie I’ve very little to say

Kisses of Fire:

this is better than the mess we got in the movie

I still haven’t forgiven them for their crimes against this song - if they wanted a joke song, ABBA has about a dozen weirdly niche songs they could have used, not one of my childhood faves

Andante, Andante:

I think they just wanted to include this song tbh - had Lily James jumped in on Kisses of Fire it would have worked better

The Name of the Game:

I find it far too interesting that they kept the change from “bashful child” to “curious child” that they made for the song in its original context in the musical

Knowing Me, Knowing You:

taking Sam’s song from the stage show and giving his younger self the same song? works so well!

having young Sam and young Donna sing together, and Sam and Sophie together? also works well!

why did they shorten the ah-has, everybody knows them so it sounds weird

Angel Eyes:

finally some good fucking food

seriously this is the first song thus far that I’ve actually put on my phone

it’s just FUN and I’ve missed Tanya and Rosie throughout, oh my god

this really highlights how much better the ensemble was done in the first movie - like it’s a bit weird how enamoured these generic young adults are with Sophie, whereas the ensemble in the first movie had a bit more age and appearance range and were a bit more of a Greek chorus as opposed to a generic workforce who really like their boss with the American accent despite her spending pretty much her entire life in Greece... but I digress

the best part of this song is definitely when Rosie and Tanya are singing

Mamma Mia:

yes! fun! happiness!

Lily’s vocals are growing on me now that I’m not looking at her being far more energetic than she sounds

seriously though let Rosie and Tanya sing too damnit

also I know it ends on the shock return of Bill but like... it doesn’t even have that energy in the recording

Dancing Queen:

let’s be real this is the most joyous scene in the movie

you’ve got the boats, you’ve got the run for the harbour, it’s pure happiness

not sure if the lack of a standout lead vocalist is a good thing or not... but it’s so damn happy (I mean it’s probably supposed to be Amanda but you can barely bloody hear her)

though you’re a teaser you turn them on / leave them burning and then you’re gone soooo sounds like they didn’t want to be saying it

I’ve Been Waiting For You:

this started and I felt like I’d been stabbed because I bawled my guts out both times I saw it (and my mum cried too which made me cry more)

and listened to the original and cried ninety seconds in

the lyrics are so so different to the original which is just disorienting

end of the first chorus and I’m crying

it’s okay but it works well in the context of the flashback (less so in the present)

Fernando:

it cracks me up that you can barely hear Andy Garcia on the rare occasion that he is singing

can’t believe his entire character was probably a device to get Cher to sing Fernando

worth it

My Love, My Life:

I’m crying right now and Meryl hasn’t even appeared

this is probably my favourite song on this soundtrack and yet it isn’t on my phone because that’s how we get me crying in public when it comes up on shuffle

Super Trouper:

the tiny medley intro to this is the sound of me trying and failing to emotionally deal with the emotional whiplash that is My Love, My Life into a goddamned concert

it’s goddamned Super Trouper and Meryl turned up and I cried

it’s a party it’s so fun but I cry nonetheless

who the fuCK gave Meryl the line “the sight of you will prove to me I’m still alive”

The Day Before You Came:

I was so baffled when this was on the soundtrack and baffled again when it wasn’t in the movie - I was expecting it in the credits. but apparently they just did it cos they could

they change “seventh cigarette” to “second cigarette” and make her work day an hour longer, she watches House of Cards instead of Dallas and reads Margaret Atwood instead of Marilyn French...

it is very oddly specific in its lyrics

though the common ideas of what exactly comes the next day tend to be like... a lover, a stalker, a murderer, death itself... so with that in mind... big yikes

In general:

I think part of the fun of the questionable singing in the first movie is that it’s a bunch of Hollywood heavyweights giving it their all and clearly having a blast while they do it. If we don’t know who they are, and their singing is a bit meh, it’s just... not quite so fun.

too many men singing

not enough energy - were they singing on set like they were in the first one? because a lot of the recordings are kinda lacking in guts

genuinely the fact that we get an extended version of Kisses of Fire that never appeared in the movie when we don’t get Hasta Mañana is going to annoy me until we get a proper recording of Helen Sjöholm singing it (prayer circle for the DVD honestly) because that was the best sung song in that entire movie

I prefer the first one but I’m far too attached to it to waver easily

[zesbian]

Sweetener- Ariana Grande review

alright so!!!! continuing my tradition of reviewing albums even tho no one asked, here’s my hot takes on Ariana Grande’s Sweetener. it’s bad folks, and I’m on mobile so sry u gotta scroll.

(Disclaimer: I am no stan. love her music, but I’m not listening to this as a ride or die. )

Raindrops - rly pretty, loved it. her voice is angelic here. it’s a cover, I don’t have much to say here lol. 3/5

Blazed - I heard this from a leak on tumblr earlier and wasn’t impressed, but I think if this became a single I could get used to it. also there’s a part where the beat reminds me of Blurred Lines which is Yikes (the “EVERYBODY GET UP” part of blurred lines). catchy but not like impressive. lyrics make no sense sry. Pharrell is annoying, like usual. 2/5

TLIC - Nicki is great here, and that’s the only reason it’s 2/5 and not one. There’s something annoying about it, but I rly like some of the lyrics. 2/5

REM - merry Christmas. this was supposed to be a Beyoncé song, look it up. “Does this end” girl idk but I wish it would. 2/5

GIAW - one of my fave songs of 2018. she did that, I’m not sorry. already screamed enough about this song everywhere else. 5/5

sweetener - the FBI tortures war criminals to this. 1/5

successful - Don’t speak too soon babe. 1/5

everytime - at this point I’m wondering if she just forgot to make the album and had to write everything and do the production in a month. the only high notes I’ve heard beside she intro and GISW and it was even boring there. 1/5

breathin - okay this one is better, I like the message and it’s relatable for me personally. I think I could learn to like this one. it’s not like incredible lyrics but they’re very personal which I like. 3/5

NTLTC - she did that. Stuck in my head for like 2 months straight when it first dropped and it’s still good. breath of fresh air in this mess. 5/5

borderline - this gives me anxiety. also it’s aggressively heterosexual. Missy couldn’t even save it, which is saying a lot. 2/5

better off - like the message of trying to move on from a bad situation, but it doesn’t rly work for me. sorry. 2/5

goodnight n go - okay this is rly cute. I rly liked the bridge and ending. 3/5

pete davidson - I’m happy she’s happy I guess. 1/5

get well soon - okay so I rly like this a lot. I read somewhere that it’s supposed to represent what it feels like for her when she went through panic attacks and disassociation after Manchester, and as someone who has panic attacks, I could really feel that. like idk if I’d listen to as a regular pop song, but I liked it as an artsy concept. really captures the feeling of trying to force yourself out of a panic attack. 4/5

only songs I downloaded/best songs (notice all but 2 are singles she already put out.... take from that what you will....anyways...):

- Raindrops

- God Is A Woman

- No Tears Left To Cry

- Get Well Soon

songs so bad they should be against the Geneva Convention:

- Sweetener

- Successful

- Everytime

- Borderline

- Pete Davidson

songs that I am just so extremely neutral towards:

- Blazed

- The Light Is Coming

- REM

- Breathin

- Better Off

- Goodnight n Go

[heartwcrk]

nate .... my fave song right now is everybody wants to love you by japanese breakfast. can i have a blog rate おねがいします 🙇🏾‍♂️

url: absolutely not | kinda cringey | it’s okay | amazing | you had to kill someone to get this. i know it.

desktop theme: please change it | maybe look for other options? | neutral/same as mobile | good | you’ve unlocked the secrets of html

mobile theme: Yikes Fam | kinda garish | not the best, but not bad | good | aesthetic as hell

icon: what is that? | alright | awesome | really cool | dude, where did you get that from??

following?: no, sorry | i am now | yes! | if i unfollow you please assume i’ve been hacked

overall content: not my thing | we’ve got some similar interests | pretty cool | i like a lot of the stuff you post | are we the same fuckin person?

other comments: chase u are amazing and ur mobile theme reminds me of autumn!! :)

[vocalfriespod]

A Sirious Problem Transcript

MEGAN: Hi, welcome to the Vocal Fries Podcast, the podcast about linguistic discrimination.

CARRIE: I'm Carrie Gillon.

MEGAN: And I'm Megan Figueroa. And today we are going to be talking about artificial intelligence generally and more specifically automatic speech recognition. We have a guest here with us, because Carrie and I do not know anything about - well, ok, I shouldn't speak about Carrie’s ignorance on the topic - but I don't know

CARRIE: You were correct: I know nothing.

MEGAN: Ok. We are joined by Dr. Rachael Tatman. She is a data preparation analyst at Kaggle, which is, according to its own Twitter account, the world's largest community of data scientists. Rachael has a PhD in linguistics from the University of Washington, where she specialized in computational sociolinguistics. Her dissertation, among other very cool things, showed the ways in which automatic speech recognition falls short when dealing with sociolinguistic variation, like dialects. Welcome Rachael.

RACHAEL: Hi! Thanks for having me.

CARRIE: Hi!

MEGAN: I'm very excited to have you. I feel like, with automatic speech recognition - I don't know if other people feel this way - but I was in the camp where I didn't realize that I should care about what's happening, with how automatic speech recognition is being made or to listen to voices. I didn't know that I had to care, and now I care. Hopefully we’ll show listeners why we should care.

RACHAEL: Yeah! I can share one of my stories about automatic speech recognition. One thing that's really difficult is children's voices, because obviously children are a different size, and they have a lot of acoustic qualities that are different. But also children have a lot of individual variation. If you spend a lot of time with kids, what's a “bink bink”? Is it a blankie? Is it a bottle? I'm Dyslexic, and when I was in grade school, they tried to use automatic speech recognition to like help me type faster, so I could complete assignments and turn them in. And not fail third grade. Yeah: it did not work well. I remember very distinctly that I tried to say “the walls were dark and clammy”. We were doing a creative writing exercise, and it was transcribed as “the wells we're gathered and planning”. Which is kinda close acoustically, but also there's some probably poor language modeling behind that, where they thought that that was a more likely sentence than the one that I'd started with.

CARRIE: Wow.

MEGAN: Lets define automatic speech recognition for the listeners, and for myself. What is automatic speech recognition?

RACHAEL: It's the computational task of taking in an acoustic signal of some kind and rendering it as speech. When I say an acoustic signal, I mean specifically a speech acoustic signal, because also people work with whale song and bird song and stuff. It gets used a lot in especially mobile devices. If you know Google Now or Cortana - I don't know how many people actually use Cortana - or a Bixby, which is Samsung's virtual assistant, or Siri, which is probably the most well-known one, they all rely on automatic speech recognition to sort of understand what you're saying and reply to your tasks. It gets used a lot in virtual assistants, which is Echo or Google Play, or Apple's launching one soon, as well. I don't know how much you guys keep up with tech news, but these are little devices that sit in your home, and you can be like, “hey, Siri”. I guess, I don't know what the Apple one’s gonna be called. Or, “okay, A L E X A”. I don't want to say it, because I don’t want to turn on everybody’s Alexa.

CARRIE: Oh no! I think you just did!

ALEXA: Hmm. I'm not sure what you meant by that question.

RACHAEL: Go back to sleep Alexa. It's everywhere, is the point. People are incorporating into new technologies. They're getting really excited about it. People are talking about incorporating it into testing for schools, for standardized testing. People are talking about incorporating it into medical diagnostic tests. Things like - what's that a semantic one, where you have to name a bunch of things that are similar, before you move on?

CARRIE: I don't know.

RACHAEL: It gets used for diagnosing a lot of things, like schizophrenia and Alzheimer's and specific learning disorders. Semantic coherence test maybe?

CARRIE: Yeah.

RACHAEL: Anyway, people have been working on using speech recognition for that, so incorporating it into this. People are using it for language assessment, for immigration and visas, a lot of very high stakes places.

MEGAN: That's very high stakes! That's very important.

RACHAEL: Probably my favorite thing to be upset about in this realm is people incorporating NLP, which is natural language processing, which is more as text, and also automatic speech recognition, in these algorithms that you put information into and it tells you whether or not you should hire the person.

CARRIE: UGH. Oh my god.

RACHAEL: So very, very high stakes applications. You may not always realize that your voice or your or your language is being used in this way.

MEGAN: You can't see my face, but I'm horrified right now. Okay. It's very important. There's a lot of practical applications that automatic speech recognition is being used for. In all of these realms, there's possibility of discrimination.

RACHAEL: Yeah. As far as I know, no one who has looked at an automatic speech recognition system or a text-based system, specifically looking at performance across different demographic groups on a certain task, has ever found that “nope there's no difference, it doesn't matter, the system is able to deal super well with people of all different backgrounds”. Looking specifically at speech, I've done a number of studies, and by a number I mean two, and I'm working off and on a third, because I am also working full-time - this isn't part of my job. I'm not speaking on behalf of my company or employer. If you're gonna yell at anybody, yell at me personally. This is my private, individual thing. What I found is that there are really, really strong dialectal differences - so differences between people who have different regional origins. Which dialects get recognized more or less accurately seems to be - I'm having a hard time picking it apart, but I think it also is a function of social class. It's fairly difficult to find speech samples that are labeled for the person's dialect and also their social class, and good sociolinguistics sampling methods. It's really hard to find large annotated speech databases that you can do this analysis with, but I found really strong dialectal differences in accuracy, with general American, or mainstream American English, or mainstream US English, or standardized American English - there's a lot of different terms for this “fancy” talk - having the lowest error rate. I found that Caucasian speakers have the lowest error rate. Looking at Caucasian speakers, African American speakers, speakers of mixed race, and the study where I had race information - I only had one Native American speaker, so I had to exclude them, because one data point is not a line. So that's worrying.

MEGAN: Right. What does it mean to have an error? What is the practical result of an error in speech recognition?

RACHAEL: There are three types of errors. One is where a word is substituted, so you say “walls” and it hears “wells” and transcribes that. Another one is deletion, where you say something like “I did not kill that man” and “I did kill that man” is transcribed. I should say people are still using hand stenographers for court cases, as far as I know. I don't think anyone in the legal system is using ASR, but yikes.

CARRIE: Better not.

RACHAEL: There’s also insertion, when you think that you heard a word and it wasn't actually there. A lot of times words that’re inserted are function words like “the” and “of”, things like that.

MEGAN: So deletion, insertion, and hearing it wrong. Doing another word.

RACHAEL: Yeah those are the only three transformations you can do, yes.

MEGAN: Okay.

RACHAEL: Word error rate is just, for all the words, how many of them did you get wrong in one of these ways. Just on a frustration level, if you're using speech recognition as a day-to-day user, and it doesn't work real great, that's annoying. I'm sure if you guys ever use speech recognition, like on your phones, or I have a Google home, and I'll use it for a timer a lot. It's actually gotten better - it used to be really bad at hearing the word “stop” like “stop the timer”. I think that might be because of the [ɑ] [ɔ] merger that some people have. That's my pet theory. But it's gotten a lot better at understanding “stop”. I would have to say “stop” five times while I'm standing at the kitchen with cheese smeared on my arms up to my elbows or whatever.

CARRIE: That's really strange because there isn't a different “stop”. I have the [ɑ] [ɔ] merger, so I can't make the other word, but it doesn't exist anyway.

RACHAEL: Yeah, it may be that the acoustic model is more - so speech recognition, I'm gonna say this generally - because people are futzing around with it a lot and I'm messing it up -generally has two modules. One is the acoustic model, which is “what waveforms map to what sounds” and the other is the language model, which is “what words are more likely”. When you when you put those together, out comes the other end through some fancy math the most likely, for some given set of input parameters, the most likely transcription, ideally. And my guess is that if you're not specifically modeling the fact that some people have two vowels and some people have one vowel in that space, you may be less able to recognize those sounds generally, because you think that there's just a lot of variation there. Especially since there's also the Northern city shift that's muddling that whole area as well. Sorry, should I assume a lot of phonetic backgrounds on the part of your speakers?

CARRIE: Our listeners? Yeah, I was just gonna say: maybe we should describe what the Northern vowel shift is.

RACHAEL: There are a number of vowel shifts in the United States, and if you think of individual vowels as being little swarms of bees that are clustered around flowers, sometimes the swarms of bees move on or the flower moves and the swarm follows after it, and different places have movement in different directions. I don't know, is that a good analogy? I'm using my hands a lot. I know you guys can't see it. Is that clear?

CARRIE: I understand what you're saying but I'm not sure. Good question.

MEGAN: I don’t know. I like the analogy. I feel like that's good.

RACHAEL: I would look up vowel change shifts, if I was listening to this. I’d just google them, and you'll see some nice pictures and arrows. You’ll be like “oh!”

CARRIE: Yeah. We’ll add something to the Tumblr to explain a little bit about vowel shifts, and also the merger we were talking about, because I can't replicate it. I can't do that open o [ɔ].

MEGAN: I can’t either. I don’t have it.

RACHAEL: “cot” [k ɔ t] as in “I caught the ball” and then “caught” [kɑt] - nope, I have it backwards again.

CARRIE: Yep. We haven't asked you yet, but what is computational sociolinguistics?

RACHAEL: I don't think I made up the term, but I'm probably one of the first people to call myself that. Dong Nguyen - she's currently at the Alan Turing Institute - has a fabulous dissertation that has a really nice review chapter that talks about the history of this emerging field. It is approaching sociolinguistic questions using computational methods, and it's also informing computational linguistics and natural image processing and automatic speech recognition with sociolinguistic knowledge. Working on dialect adaptation, I think would fall within that - that's when you take an automatic speech recognition system that works on one dialect and try to make it work good for other dialects as well. I've done some work on modeling variation in textual features by social groups. I've looked at political affiliation and punctuation and capitalization in tweets, and there's pretty robust differences at least in the US between oppositional political identities. I'm trying to think of other people's work, so it's not just: here's a bunch of stuff that I've done!

MEGAN: Basically, everyone's trying to model everything.

RACHAEL: Basically. Or should be, hopefully. I think, historically, there hasn't been a lot of - I think sociolinguists are much better about knowing what's going on in computational linguistics then computational linguists are at knowing about what's going on in sociolinguistics. I'm coming from sociolinguistics and coming to computational linguistics. I'm trying to have a big bag of Labov papers and toss them to people, be like “here you go! Here you go!”

MEGAN: Yes and Labov is a very famous sociolinguist.

RACHAEL: He is, yes. I would call him the founder of variationist sociolinguistics - which is not the only school, but it is the school that I work in mainly.

CARRIE: Yeah, I think that's - well that's the most famous one as far as I know.

MEGAN: Yeah. I didn't know there were other ones. Of course there is.

RACHAEL: Yeah, I'm trying to think of names. Mostly I'll come across it I'll be like “oh”. I guess discourse analysis is a type of sociolinguistics.

MEGAN: Oh, okay.

CARRIE: Yes.

RACHAEL: But different bent.

CARRIE: How is automatic speech recognition trained to understand humans? I think you've already started to answer this, but maybe you can answer it'll be even more, if there is more to say.

RACHAEL: Yeah. I mentioned there are two components: there's the acoustic model and then there the language model. Usually the language model is actually trained on texts. You take a very, very, very large corpus. I think right now - I don't know about the standard, but what I think most people would like to use would be the Google trillion word corpus, which is from scraped web text, or people use the Wall Street Journal corpus, which is several hundred million words long. You know the probability of a certain set of words occurring in a certain order, so it's the poor man's way of getting syntax. I'll tell you about how it's traditionally done. People are replacing both the pronunciation dictionary and the acoustic model, which sometimes includes the pronunciation dictionary with big neural nets. We can talk about that in a little bit, but traditionally the pronunciation dictionary was made by hand. The Carnegie Mellon the pronunciation dictionary, or CMU pronunciation dictionary, is probably the best-known one for American English. People transcribe words, and if there's one that you need that's not transcribed, you add it.

MEGAN: And what’s a pronunciation dictionary?

RACHAEL: It is a list of words and then how they're pronounced. The phones, so “cat” would be [k] [æ] [t] - those three sounds in order. Then the acoustic model takes the waveform and tells you the probability of each of those sounds. So it's like “well I'm pretty sure it's [æ], but I guess it could also be [ɑ]”, through a process of transformations. People recently have been taking a speech corpus - usually one that's labeled, so you know what words are spoken - and then using all of that data and shoving it into a neural net, which is a type of machine learning algorithm - it's a family of machine learning algorithms. People use different types and flavors, and they have different structures. What neural nets are really, really good at is finding patterns in the data, and recognizing those same patterns later, without you having to tell them to do it. They learn it themselves, from just the way that the information is organized. They've been really, really good and useful in image processing, in particular, being able to look at a photo and be like “here is an apple”, “here is an orange” and “I have circled them helpfully for you”. They're really good at that. But as it turns out there is more structure in language than there is in other types of data.

CARRIE: Shocking. [sarcasm]

RACHAEL: It is to some people. I've had a lot of frustrating conversations where people were like “but it works really good on images!” I'm like “yes, but language is different”. If it weren't, we wouldn't need linguistics. People wouldn't need to study language their entire lives, if it was just like images but in sound, basically. Which I think is probably not news to any listeners of this podcast, but definitely it is news to some people. Neural nets are really good at seeing things that they've seen before, or identifying the types of things they've seen before, and if they see new things, they're not so good at it. I think that's really where a lot of the trouble with dialect comes in, because sociolinguistic variation is very systematic between dialect regions. One person can have multiple dialects as well. I don't want to make it sound like you sort people into their dialects and then apply the correct model and then boom everything's correct all the time. Because people have tried that and it works better than not doing anything, but it's still not - I don’t know. There's a lot of work to do, and I don't want to make it sound like speech research engineers are just fluffing around and not knowing about language, because they do. But it's difficult, and it hasn't, I think, been a major focus for a lot of people recently, and I'm hoping that it will become more of a research focus.

MEGAN: You said something in one of your interviews that I wanted to read here that I liked. You say that “generally the people who are doing the training aren't the people whose voices are in the dataset. You'll take a dataset that's out there that has a lot of different people's voices, and it will work well for a large variety of people. I think the people who don't have sociolinguistic knowledge haven't thought about the demographic of people speaking would have an effect. I don't think it's maliciousness. I just think it wasn't considered.”

RACHAEL: Yeah.

MEGAN: I think “it was a considered” part - it's how I felt actually. I obviously very much care that people aren't discriminated against in every aspect of life. But I just didn't think about speech recognition.

RACHAEL: Yeah. I think we have this idea that like “oh a computer’s doing it, so it's not gonna be biased”.

MEGAN: You’re right.

RACHAEL: That’s nice to believe that you have the ethical computer from Star Trek, but bias is built into all machine learning models. It's one of the things you study in a machine learning class. You talk about bias and variance, and it's there in the model, and it's there in the data. Pretending that it can go away if you just keep adding more data is a little bit of a problem for the people who are actually using the system, and it doesn't work as well for them as it should, maybe.

CARRIE: It's also very naïve.

MEGAN: Yeah. Humans are the ones that are doing it, right. We’re behind the machines. Of course there's biases. I was thinking, I've said I've never thought about this before, but I don't use Siri, because Siri does not understand me very well at all. I've given up.

SIRI: I miss you Megan.

MEGAN: I didn't take the next step. I didn't take the next step, and think “oh why is this the case that she's not understanding me very well”.

RACHAEL: Yeah.

CARRIE: She understands me pretty well. I have a pretty standard North American accent.

RACHAEL: A little bit of the Canadian shift.

CARRIE: I do, but it's not enough to trick SIRI, apparently. My accent has shifted somewhat since living in the States for over nine years. I knew that speech recognition did have a problem with at least some dialects, because there's a fairly famous skit from Burnistoun, the Scottish sketch comedy show, where he's just saying “eleven”, and it's one of the words where in a Scottish accent “eleven” is pretty close, so the speech recognition should have been able to pick it up. Most of the sketches is them speaking in a Scottish dialect that I think many Americans would not understand actually.

IAIN CONNELL: You ever tried voice recognition technology?

ROBERT FLORENCE: No.

IAIN CONNELL: They don't do Scottish accents.

ROBERT FLORENCE: Eleven.

ELEVATOR: Could you please repeat that.

ROBERT FLORENCE: Eleven.

IAIN CONNELL: Eleven.

ROBERT FLORENCE: Eleven. Eleven.

IAIN CONNELL: Eleven.

ELEVATOR: Could you please repeat that.

IAIN CONNELL: Eleven. If you don't understand the lingo, away back home your own country. [If you don't underston the lingo, away back hame yer ain country.]

ROBERT FLORENCE: Oohh, is the talk now is it? “Away back home your own country?” [Oh, s'tha talk nae is it? "Away back tae yer ain country"?]

IAIN CONNELL: Oh, don't start Mr Bleeding Heart – how can you be racist to a lift? [how can ye be racist tae a lift?]

ELEVATOR: Please speak slowly and clearly.

CARRIE: Anyway, it's a really funny sketch, if you haven't seen it. I will post it, because I think it's funny.

MEGAN: I don't know what it is about me. I don't know if vocal fry would affect it at all. I'm also kind of mumbly. I try not to be mumbly on the podcast obviously, but in my normal everyday life, I am a mumbler, so that might be it. I expect Siri to understand my mumbles, but she don't, so I gave up.

RACHAEL: But see, that's part of the problem, because - I don't know for sure, but I would be beyond shocked if - because I know that for sure, Google has the ability to - it retains the speech samples that you send them, and I'm sure that they fold them back into their training data, so if you're not using it, because it doesn't understand you, it's pretty much never gonna understand you, is the unfortunate thing. I think that's really part of the reason that there's - I think - pretty strong class effects. This is this is me having a science hunch that I haven't really banged out yet in some experimental work. I think that people who have a higher socioeconomic status and particularly professional class, mobile - not rural the other one.

CARRIE: Urban.

RACHAEL: Urban! Yeah, thank you. Especially professional, mobile, urban people have - I'm almost positive - higher cognition rates, correct word rates.

MEGAN: You mentioned something about how the language model was taking in things like The Wall Street Journal. Wouldn't that affect it too? That's not your acoustic signal, but it's the way you speak? I don't know.

RACHAEL: Yeah. No that's fair. “‘Fiduciary’ seems to be a fairly common word that humans use all the time, so I’m gonna look for that one.”

CARRIE: I would be very surprised if class didn't play a role. It always does. In everything that we talk about, there's something about class going on too. But we don't think about it as much in North America as we should.

MEGAN: We really don't. Especially since it's wrapped in with race and ethnicity so much. I act like I know anything beyond the States. It's just very American.

RACHAEL: I think it's very much the top-level thing that people think about with language variation in the UK, for sure.

MEGAN: Ah, okay.

CARRIE: Yeah. Absolutely.

MEGAN: Interesting.

RACHAEL: There's RP, and then those weird regional dialects that we don't like. As a person not from the UK, that's the judgments that I've gotten from consuming popular media.

CARRIE: It used to be worse. Because the BBC used to only have received pronunciation with their reporters, but now you'll hear regional varieties. Still the most prestigious versions of those varieties, but at least you'll hear Irish dialects now. Things are slightly better.

MEGAN: You'd hope so. ASR is trained to understand humans, so you're feeding in them these datasets, and I didn't know this but I guess, like you said, if I talk to Siri, I'm also feeding into a dataset.

RACHAEL: Yeah. That seems very likely to me. Again, I don't know for sure, and this may be something that's Googlable, you could find using a search engine, and it may be something that you could not find using a search engine. The other thing about neural nets is because they're good at seeing things they seen before, they get really good if you have a lot of data, a lot of data. I have not yet seen the company that would ignore free data that people were giving to it to improve model performance.

MEGAN: Do you have examples of automatic speech recognition failing to understand people that we can give the listeners, so they can see the problem?

RACHAEL: I can give you one from my life, which continues to drive me nuts. I'm from the South and I have a general American professional voice that I use, but especially if I'm relaxing with friends or with my family, I definitely sound more Southern. One of the things that happens in the South and also in African American English is nasal place assimilation. If you have a nasal after a stop, which are sounds like [k] [t] [p] [g] [d] [b], you will change the nasal, [m], [n], or [ŋ], to whatever the thing in front of it was. I would say “beanbag” as “beambag”, especially in an informal setting. Or a “handbag” is “hambag”. Put your things in your handbag. I think it's a fairly common thing. Google used to always, always, always search for “beambag” when I wanted to know about ���beanbags”, because I was doing research to get - I currently have one, I just turned to look at it - a really good beanbag chair. They’re very comfy! I like them. It kept telling me about “beambags”, which are not a thing! It just drove me up the wall, because lots of people do this thing. This is a normal speech process.

CARRIE: Yeah. Very common.

MEGAN: Also, a “hambag”, a bag of hams and that might be something people have.

RACHAEL: I guess a Smithfield ham does come in a bag. It comes like a little canvas bag.

MEGAN: I guess that's where it's trying to get you. But that's not what you’re [meaning]. That’s funny. Okay, how do we solve this problem? What should we be thinking about when we develop automatic speech recognition databases and such? Who should be involved?

RACHAEL: Sociolinguists. Definitely hire sociolinguists. That's my general go-to drum. It's a hard problem. I don't want to pretend that a sociolinguist looks at it and they're like “ah! Fix this parameter!” and then suddenly it works great for everyone. Because the fact of minority languages or language varieties, in particular, is that they’re minority because fewer people use them. If you are trying to optimize performance and accuracy for the model as a whole, and you raise it for the people who are from minority groups - whatever those may be - if you are using the one model, that will lower it for your majority language speakers. Just adding more data isn't necessarily going to be the fix. People have been have been working on this for a long time, and it's a very hard problem, and I have nothing but respect for everyone who's working on this. There's a couple of approaches that people are doing. One is to train multiple models on different stable language varieties. In the US I might train one on West Coast generally, and as far as that is a single language variety, I’d probably train one on the Northeast, one on the northern cities, so Chicago, Michigan sort of area - Chicago's in Illinois - Illinois, Michigan sort of area. One on the South. One also for the mid-Atlantic region. And then select one of those models, based on whichever would most accurately represent the person who's speaking. That's one approach. Another approach is to take the model and then change it for every single person's voice. That will capture dialectal variation, but it will also capture individual variation. The reason that your phone doesn't do that automatically is because it is very computationally intensive. These models are very big. They have a lot of information in them. They have a lot of parameters, and to change those, it takes a lot of raw processing power. That's not really feasible to do for individual people, as it stands. I don’t know, maybe in five years it will be completely feasible. We’ll all have GPUs falling out of our pockets everywhere we go. I don't know. That's another approach that some people have taken. I don't know, maybe with some fancy new ensembling - which is where you multiple different types of models and stick them together like - what are those, K’nex? - and they build a pipeline, and then you shove the data all the way through the pipeline, and all the different models that are connected together. Those have been getting really good results lately, so maybe some sort of clever ensembling, where you do something like demographic recognition, and then something like shifting your language model a little bit. I don't know. I don't know. I don't know what people are gonna come up with.

MEGAN: This is the future. This is the future that millennials want or something. I don’t know. This is the future liberals want. If this is the future, I'm thinking about the fact that in 30 years we're gonna be a majority-minority country. We're on our way to this becoming a bigger and bigger problem.

RACHAEL: Yes. Definitely.

MEGAN: The fact that Siri or Alexa - sorry - has trouble understanding people that aren't in this white -

RACHAEL: Super-privileged, small group?

MEGAN: Yeah, right. There's a gender bias too, right? It's males that are understood.

CARRIE: And we're the majority.

RACHAEL: I just want to quickly intercede here - I did some in earlier work finds that it was more accurate - specifically YouTube's automatic captions were more accurate for men than women, but I think, because I couldn't replicate that result, the problem there was actually signal-to-noise ratio. Women tend to be a little bit quieter, because we're a little bit smaller. If you are speaking at the same effort-level in the same environment, there's just gonna be a little bit more noise in the signal for women, because we're not quite as loud. I don't know that clutter signal processing can fix that. I'm gonna keep working on this, and who I might find out that actually there are you know really strong differences, it maybe it can't deal with things that women do more. I was gonna say “vocal fry”, but I've seen no evidence that women fry more than men, which I'm sure you talked about. At length.

CARRIE: Right. That was our first episode. Everybody does it. Leave us alone!

MEGAN: Leave it alone. Get the fuck off my vocal fry! What I'm hearing is this is something that we should all very much care about, because, like Carrie said, everyone else is the majority. If it's best trained on white men that are in higher socio-economic classes, that's not the majority. It sounds like we need to have people in the room, because, like you said, you don't think it was considered when they were making these datasets. We need people in the room that are like “wait, I come from this community where that's not how we talk, this is not gonna work for me or us”.

RACHAEL: Yeah, definitely.

MEGAN: I definitely want to plug a representation too. We need more people in the room.

RACHAEL: Definitely. I've been talking about English, because that's what I know about, and specifically American English. I don't want to get into British dialectology, cuz that's crazy, crazy complex. But this is also a problem in other languages. Arabic dialects are incredibly different from each other.

CARRIE: Right.

MEGAN: Now I'm thinking about people that are bilingual.

RACHAEL: Or bidialectal.

MEGAN: Or bidialectal, for sure. That's gonna be something else that we would want automatic speech recognition to recognize.

RACHAEL: Yeah. Absolutely. I can give people something that you can do right now - is that Mozilla, which is the company that owned the Firefox - continues to own, I think, the Firefox web browser - is currently crowdsourcing a database of voices, and voice samples. You can head over to that website, for which there is a link that I for sure can't find. I think it's called the Mozilla Common Voice Project, but don't quote me on that unless it's right.

MEGAN: We'll put it somewhere.

RACHAEL: Mozilla is doing a collection of voices of people, and they're specifically trying to get people from different demographic backgrounds, for specifically this problem, for knowing demographic information about someone, for having speech samples for the. They're also having people manually check the recording, so if this is something that's interesting, and you want to listen to a lot of voices, I'd recommend heading over there and checking it out.

MEGAN: Ah, so they are crowdsourcing automatic speech recognition. That's a good idea. That’s a tough - how you get the most variation in the people that reply.

RACHAEL: One thing that I found in my own work, and other computational linguists have found as well, is that we know a lot about variation in speech, but a lot of the same variation also exists in text. A lot of the text that you produce in your day-to-day life, especially if it's anywhere online, is getting fed into a lot of natural language processing tools. There are also problems with those. Things like identifying what language someone is using is not as good.

CARRIE: Yeah, I notice that on Twitter a lot. It wants to translate from French all the time.

MEGAN: Yeah.

RACHAEL: Twitter's language ID is a hot mess. A hot mess.

CARRIE: And it's never French. It's never French. In fact, sometimes it's English. I'm like “what is going on?”

MEGAN: I've had Estonian. Translate from Estonian.

RACHAEL: Yeah. Estonian tends to show up a lot. I'm trying to think of - I have started doing some very lackadaisical data collection. I think it seems to work on a character level, so it tends to be fairly good at languages that have a unique character set. It tends to be very good at Thai, but related Germanic languages - pfft - it does not. That's Bing. That's on Microsoft. They're the back end there, so I 100% blame them. Maybe, if they hadn't gutted their research teams, they would be able to do this better.

CARRIE: Hint hint.

MEGAN: That is something that we can do immediately. Do you have something really poignant you want to say about why this is all important? What's the takeaway message? Because we've been talking us this whole time about why it's important, but what do you think is the takeaway?

RACHAEL: It's important to hear people's voices. Both literally and metaphorically.

CARRIE: There we go. There's the money shot.

MEGAN: That’s the money shot. Money, money, money. See that's what we wanted!

CARRIE: Yes. It's important to hear people's voices. I think that's a good place to end.

MEGAN: Yeah, cuz that was it. Unless you have anything else, Rachael?

RACHAEL: Hmm. No, I don't think so. I use my hands a lot, so hopefully a lot of the things that I was saying with my hands I was also saying with my voice.

MEGAN: Yeah, I realized that at our first episode, I was using my hands, and now my hands don't even move. It comes with some experience - of my four episodes that I have done, five episodes.

CARRIE: Five! Five episodes. This is our sixth.

RACHAEL: Ooh! Lucky number 6!

CARRIE: Thank you so much, Rachael, for talking with us today.

RACHAEL: You’re welcome!

CARRIE: That was awesome. I learned a lot.

MEGAN: I know, I learned so much. I was so ignorant on this subject. So thank you. Hopefully this will be of interest to people that have no idea, but also to our listeners that really like speech recognition stuff. I know that I know that they're there. This is very exciting. Alright, cool. I guess we want to leave everyone with one message, which is: don't be a fucking asshole.

CARRIE: Don't be an asshole. Bye!

CARRIE: The Vocal Fries Podcast is produced by Chris Ayers for Halftone Audio. Theme music by Nick Granum. You can find us on Tumblr, Twitter, Facebook and Instagram @vocalfriespod. You can email us at vocalfriespod@gmail.com.

[shslshortie]

Little log 8/9 (please ignore)

Again, this has absolutely nothing to do with like any of my followers, so please ignore it cuz it'll just clog your dash.

Luckily, I'm posting this at 4:57 am, so not THAT many people will have to see this.

BUT Kaja asked how this whole thing was going, so I figured I'd update this thingy with the stuff that happened Saturday and Sunday.

Current list:

1) Christianna

1) Kelsey

1) Rae

(Everybody else is mostly the same?? Or my opinion hasn't changed THAT much to where I need an update on them)

I don't know how to do a read more on mobile???? So sorry?????????

++++++++++++++++++++++++++++++++

Okay, so Saturday was rough for me. This weekend had a lot of things kinda up in the air, and most of them didn't turn out the right way, so I was not in a good mindset.

But while I was alone and upset and feeling fairly unwanted, OUT OF NO WHERE WITH NO PROMPTING, both Christianna and Kelsey were texting and snapchating me like everything was completely normal (which it was for them). This was amazing, because when I felt so shitty, they were still reaching out to me just because they wanted to, or because they had stuff that they specifically wanted to tell/show me. Kelsey was even saying how she wished I was a dance major just so I could have been with her at the dance major watch party. And she was the one who convinced me to go to the party on Saturday night. (Which was not good, especially since she ended up leaving with the only other member that I've heard she wants as her big, so YIKES). ((I mean if Madison (the member) wants Kelsey, there is literally nothing I would be able to do to stop her from taking her. Everybody would want Madison if they thought she would want them, and I wouldn't even be able to be mad about it, because she is perfect))

So I happy cried over both Christianna and Kelsey because it made me feel wanted by both of them, and kind of solidified the relationship I want to have with them. A two way street of care and love, where even though I will basically be their mom, where at the basis of our Big/Little relationship, we are still really good friends.

And with Rae, last week she was apparently looking for me at the tailgate because she didn't know anyone else there, and because I was the only one who she felt comfortable around who wasn't black out drunk. (Bad thing was, I showed up real late and she had already left, BUT I ended up seeing her in the stadium and we talked for a bit!!) And yesterday and today she was asking me for advice about guerrilla; and me being the Pledge Mom™ that I am, offered to bring any pledges who wanted it food/coffee while they were waiting in line for guerilla sign ups. I ended up staying the whole time??? (Which was stupid and unnecessary but it was fine) and for most of it, she was just telling the other pledges how wonderful I was.

This is a huge ego boost, but the problem is that if these rankings stay the same for me... I have no idea who I would pick. Because if I decided to put Christianna and Rae as my #1 and #2, I would probably have guaranteed twins with both of them. Currently, I want Kelsey a bit more, just because I feel like it would be a better Big/little relationship, since Rae doesn't drink or do a lot of stuff like that, and I don't want to end up with a little that judges me or is ashamed of me. BUT I know that if I end up putting Kelsey as like #1 because I want her most, and put the other two at 2 and 3.... I might not get any of them... and even though I feel like I would get at least 1 little that I love, even if they aren't on my list... I don't want to mess it up with any of them because I made a mistake in ranking.

So long story short @ me:

Christianna: I'm like 90% sure I'm gonna get her. We are planning a guerrilla act together, and I was the first/only person she thought to ask. She doesn't know that many people, so I have absolutely no clue who else would be on her list. AND WHEN I WAS COMPLAINING I DIDNT HAVE ANY BLUE GLITTER, SHE SAID SHE HAD SOME THAT I COULD USE. and especially with my reputation in APO (my fucking pledge name was Sparkle Tits for crying out loud) there's no way it wouldn't be a match.

Kelsey: I love her, and we snapchat all the time, and she is super fun, and we will be going on a second pledge date soon. I'm just worried because she might want a Dance big, especially if she ends up with Claire as her dance honor society big... so I'll just have to figure out if she wants Madison or not and if Madison wants her. Which is what I'm stressed about, cuz I thought Madison would want someone like Blaire or Olivia or Emma or Nicole (the upperclassman dance majors who are pledging). And since she's 1) a senior, 2) is in a sorority... I can't imagine that she will take twins??? And she hasn't even been around that much?? Only at guerrilla and popping in at parties??? So like??? HOPEFULLY I can hear the whole situation when we have family brunch.

(Which is a WHOLE different monster of stress)

Rae: love her, and she's very sweet, but what people usually think of her as is a little pompous or know it all or above it all, which isn't really true. She just doesn't drink/party, and doesn't really like it when people do. She can tolerate it, which must mean she doesn't care that I do/has never been able to tell when I've been drunk (cuz I've definitely been drunk around her at least 3 if not 4 times). And she has a very matter of fact way of talking, which kind of stems from her education/how she wants to go all the way to get her PhD and her interests, which can make it seem like she's being short with you. And I don't have a problem with it, but like I'm not entirely sure if I would be the best big for her needs??? But we've talked a lot about makeup and dance and everything, and she's been very grateful whenever I've done anything for her or offered her help, and has reached out a lot, which is probably why I'm more drawn to her right now.

Again, the biggest stressor right now, is trying to figure out the order (which Luckily, I don't have to figure out until at least the 25th, if not the end of October) so that I feel like my twins both have me as their #1, and that they are who I want. Because originally, I was very much keeping my mind open, so that I would be happy and love whoever I got. Of course, if I got someone else, that means that they wanted me, I just might still be hurt or a bit upset if I was dead set on having a particular pair, and ended up with someone else. Because the biggest thing is, I don't want to be that big that has an obvious favorite twin and an ignored twin -- because looking at it last year, it fucking SUCKED when I saw some of the pairs, and I felt bad for some of the twins because of how obvious it was. Overall, it matters what the pledges want, so no matter what, I will do my best to make them happy, give them all the love they deserve, and give them the best APO experience.

Last note:

Holy hell am I stressed about this upcoming family brunch. We've been trying to plan it for 2-3 weeks, and it still hasn't happened yet. I'm very worried specifically because I don't want to be split off from the Peyton-Carli line just because LC is gone. Unless EVERYONE in our family gets twins, we wouldn't be strong enough on our own. Peyton and Carli would be fine, and could even still be too big. But for LC's line, it's just me and Brannon who are going to take littles. (IF MARISSA TOOK A LITTLE I WOULD BE FUCKING SHOOK. Girl Peyton could, but I would highly highly highly doubt it) and that would mean it would be like 4 people on that stage, maybe 5, maybe 6. Plus I don't want fake to split again, unless we did Peyton, Carli and LC all separate, and then came back together once everyone was done.

I also am stressed because at the very least, the entirety of Fake Fam (16 active members who attend UA currently) could theoretically take 32 new littles. At the very least, it could be 7, but it would most likely be between 10-17. That is a LOT. Which means that unless we all did the same thing (instead of the same theme like we did last year) it would be super fucking difficult to coordinate a reveal.

Finally, I'm stressed because I don't know who everybody wants???? And I KNOW that will be a huge big deal at brunch because Carli and Peyton are literally in charge of assigning Big/Little pairs for the entirety of APO. So hopefully, that means they will put us on high priority... but it COULD also mean that theyre gonna put their littles/grand littles/future ggrand littles on high priority above LC's line. ESPECIALLY if some people on their side wants the same people as Brannon or I do. (Cough cough Madison).

Best case scenario, Carli just goes around and asks everyone "do you want twins, and who do you want" and nobody has any issues with anybody else's picks.

Bad case scenario: the first thing they bring up is a split. Madison wants Kelsey and only Kelsey. I end up getting 1 little who is a random, and Brannon gets none. And drama happens where people start explaining why certain people shouldn't be a big little pair.

Well, here's hoping that it all works out, and I figure things out further. ✌

[fiirelords]

i say go 4 the url! and im on pc but my fave is the koala (isnt everybodys) (koala koala koala)

yes!! a bunch of people said koala dude!! tysm!!

lets celebrate 3.6k/3.7k! no more pls

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