G: but they didn't have them for breathing in .
G: Uh D: Yeah .
F: That 's interesting .
F: Well , what I wondered is whether it 's possible to have  to maybe use the display at the beginning
F: to be able to to judge how how correctly A: Yeah .
F: I mean , have someone do some routine whatever ,
F: and and then see if when they 're breathing it 's showing .
F: I don't know if the if it 's A: I mean , when when it 's on , you can see it .
G: I A: You can definitely see it .
F: Can you see the breathing ?
F: Cuz I A: Absolutely .
F: Oh .
A: Absolutely .
G: Yeah .
G: I
A: And so , you know , I 've I 've sat here and watched sometimes the breathing ,
A: and the bar going up and down ,
A: and I 'm thinking , I could say something ,
A: but
G: I mean , I think A: I don't want to make people self - conscious .
A: Stop breathing !
G: It it 's going to be imperfect .
G: You 're not gonna get it perfect .
F: Yeah . Uh - huh .
G: And you can do some , uh , you know , first - order thing about it ,
G: which is to have people move it , uh , uh , a away from being just directly in front of the middle
D: Yeah .
F: Good .
G: but not too far away .
F: Yeah , i
G: And then , you know , I think there 's not much G: Because you can't al you know , interfere w
G: you can't fine tune the meeting that much , I think .
A: Right .
G: It 's sort of D: Yeah .
F: That 's true .
F: It just seems like i if something l simple like that can be tweaked and the quality goes , you know , uh , dramatically up , then it might be worth doing .
A: Yep .
A: And then also the position of the mike also .
A: If it 's more directly , you 'll get better volume .
D: Yeah .
A: So so , like , yours is pretty far down below your mouth . Yeah .
F: But F: Mm - hmm .
F: My my feedback from the transcribers is he is always close to crystal clear and and just fan fantastic to C: Yeah .
D: Mmm , yeah .
G: Mm - hmm .
A: I don't know why that is .
F: Well , I mean , you Yeah , of course . You 're you 're also F: uh , your volume is is greater .
F: But but still , I mean , they they say A: I 've been eating a lot .
F: I it makes their their job extremely easy .
G: Uh .
F: Yeah .
G: And then there 's mass .
F: Mm - hmm .
G: Anyway .
F: I could say something about about the F: Well , I don't know what you wanna do . Yeah .
G: About what ?
F: About the transcribers or anything or ?
F: I don't know .
G: Well , the other G: why don't we do that ?
B: But , uh , just to to , um B: One more remark , uh , concerning the SRI recognizer .
B: Um .
B: It is useful to transcribe and then ultimately train models for things like breath ,
B: and also laughter is very , very frequent and important to  to model .
G: Mm - hmm .
B: So ,
B: if you can in your transcripts mark A: So ,
A: mark them ?
B: mark very audible breaths and laughter especially ,
C: Mmm .
F: They are .
B: um F: They 're putting Eh , so in curly brackets they put " inhale " or " breath " .
B: OK .
B: Mm - hmm .
A: Oh , great .
F: It they and then in curly brackets they say " laughter " .
F: Now they 're  they 're not being awfully precise ,
F: uh , m So they 're two types of laughter that are not being distinguished .
B: Mm - hmm .
F: One is when sometimes s someone will start laughing when they 're in the middle of a sentence .
B: Mm - hmm .
F: And and then the other one is when they finish the sentence and then they laugh .
F: So , um , I I did s I did some double checking to look through F: I mean , you 'd need to have extra
F: e extra complications , like time tags indicating the beginning and ending of  of the laughing through the utterance .
B: It 's not so I don't think it 's , um F: And that and what they 're doing is in both cases just saying " curly brackets laughing " a after the unit .
B: As as long as there is an indication that there was laughter somewhere between two words I think that 's sufficient ,
C: Yeah .
F: Good .
F: Oh !
F: OK .
A: Against they could do forced alignment .
B: because
C: Yeah .
B: actually the recognition of laughter once you kn um you know , is pretty good .
B: So as long as you can stick a you know , a t a tag in there that that indicates that there was laughter ,
A: Oh , I didn't know that .
F: OK .
B: that would probably be , uh , sufficient to train models .
A: That would be a really interesting prosodic feature ,
F: Then F: And let me ask y
D: Yeah .
F: and I gotta ask you one thing about that .
A: when F: So , um ,
B: Hmm .
F: if they laugh between two words , you you 'd get it in between the two words .
B: Mm - hmm .
F: But if they laugh across three or four words you you get it after those four words .
B: Right .
F: Does that matter ?
D: Yeah .
B: Well , the thing that you is hard to deal with is whe when they speak while laughing .
B: Um , and that 's , uh I don't think that we can do very well with that .
A: Right .
B: So D: Yeah .
B: But , um , that 's not as frequent as just laughing between speaking ,
F: OK .
B: so A: So are do you treat breath and laughter as phonetically , or as word models , or what ?
G: Uh is it ?
D: Huh .
F: I think he 's right .
D: I I think it 's frequent in in the meeting .
F: Yeah .
B: We tried both .
B: Uh , currently , um , we use special words .
B: There was a there 's actually a word for uh , it 's not just breathing but all kinds of mouth A: Mm - hmm .
A: Mouth stuff ?
B: uh , mouth mouth stuff .
B: And then laughter is a is a special word .
A: How would we do that with the hybrid system ?
G: Same thing .
B: Same thing ?
A: So train a phone in the neural net ?
B: Yeah .
B: Yeah . You ha Oh . And each of these words has a dedicated phone .
G: No A: Oh , it does ?
B: So the so the the mouth noise , uh , word has just a single phone ,
B: um , that is for that .
A: Right . So in the hybrid system we could train the net with a laughter phone and a breath sound phone .
G: Yeah .
B: Yeah .
G: I mean , it 's it 's it 's always the same thing .
B: Yeah .
G: Right ?
F: Mm - hmm .
G: I mean , you could you could say well , let G: we now think that laughter should have three sub sub sub - units in the the three states , uh different states .
C: Yeah .
G: And then you would have three G: I mean , you know , eh , eh , it 's u
B: And the the pronun the pronunciations the pronunciations are l are somewhat non - standard .
A: Do whatever you want .
G: Yeah , yeah .
D: Yeah .
D: No .
B: They actually are B: uh , it 's just a single , s uh , you know , a single phone in the pronunciation ,
B: but it has a self - loop on it ,
B: so it can A: To go on forever ?
B: r can go on forever .
A: And how do you handle it in the language model ?
B: It 's just a it 's just a word .
B: We train it like any other word .
A: It 's just a word in the language model .
A: Cool .
B: Yeah .
B: We also tried , um , absorbing these uh , both laughter and and actually also noise ,
B: and , um D: Yeah .
B: Yes .
B: OK .
B: Anyway . We also tried absorbing that into the pause model B: I mean , the the the model that that matches the stuff between words .
G: Mm - hmm .
B: And , um ,
B: it didn't work as well . So .
A: Huh .
A: OK .
F: Mm - hmm .
A: Can you hand me your digit form ?
B: Sorry .
A: I just wanna mark that you did not read digits .
G: OK .
G: Say hi for me .
F: Good .
F: You you did get me to thinking about F: I I 'm not really sure which is more frequent ,
F: whether f f laughing F: I think it may be an individual thing . Some people are more prone to laughing when they 're speaking .
G: Yeah .
G: Yeah . I think A: I was noticing that with Dan in the one that we , uh we hand tran hand - segmented ,
F: But I can't D: Yeah .
A: that th he has these little chuckles as he talks .
F: Yeah . OK .
G: I 'm sure it 's very individual .
G: And and G: one thing that c that we 're not doing , of course , is we 're not claiming to , uh , get be getting a representation of mankind
G: in these recordings . We have this very , very tiny sample of of D: Yeah .
F: Yeah .
D: Yeah .
A: Speech researchers ?
G: Uh , yeah .
G: And  Yeah , r right .
D: Speech research .
G: So , uh , who knows .
G: Uh G: Yeah . Why don why don't we just since we 're on this vein , why don't we just continue with , uh , what you were gonna say about the transcriptions
G: and ?
F: OK .