PhD C: Uh , that 's it , I think . Mmm .
PhD F: Do you have anything , Morgan , or ?
Professor B: Uh , no . I 'm just , you know , being a manager this week . So .
PhD F: How about you , Barry ?
Grad A: Um , still working on my my quals preparation stuff . Um , so I 'm I 'm thinking about , um , starting some , uh , cheating experiments to , uh , determine the , um  the relative effectiveness of , um , some intermediate categories that I want to classify . So , for example , um , if I know where voicing occurs and everything , um , I would do a phone um , phone recognition experiment , um , somehow putting in the the , uh the perfect knowledge that I have about voicing . So , um , in particular I was thinking , um , in in the hybrid framework , just taking those LNA files , and , um , setting to zero those probabilities that , um that these phones are not voicing . So say , like , I know this particular segment is voicing , um , I would say , uh , go into the corresponding LNA file and zonk out the the posteriors for , um , those phonemes that , um , are not voiced ,
PhD F: Mm - hmm . Mm - hmm .
Grad A: and then see what kinds of improvements I get . And so this would be a useful thing , um , to know in terms of , like , which which , um which of these categories are are good for , um , speech recognition .
PhD F: Hmm . Mm - hmm .
Grad A: So , that 's I hope to get those , uh those experiments done by by the time quals come come around in July .
PhD F: So do you just take the probabilities of the other ones and spread them out evenly among the the remaining ones ?
Grad A: Yeah . I I I was thinking OK , so just set to set to some really low number , the the non - voiced , um , phones .
PhD F: Mm - hmm .
Grad A: Right ? And then renormalize .
PhD F: Mmm .
Grad A: Right . Yeah .
PhD D: Mm - hmm .
PhD F: Cool . That will be really interesting to see , you know . So then you 're gonna feed the those into some standard recognizer .
Grad A: Mm - hmm .
PhD F: Uh , wh are you gonna do digits
Grad A: Yeah , m Um , well , I 'm gonna f work with TIMIT PhD F: or ? With TIMIT . OK .
Grad A: TIMIT uh , phone recognition with TIMIT .
PhD F: Mm - hmm .
Grad A: And , um PhD F: Oh , so then you 'll feed those Sorry . So where do the outputs of the net go into if you 're doing phone recognition ?
Grad A: Oh . Um , the outputs of the net go into the standard , h um , ICSI hybrid , um , recognizer . So maybe , um , Chronos
PhD F: An - and you 're gonna the you 're gonna do phone recognition with that ?
Grad A: or Phone recognition . Right , right .
PhD F: OK , OK . I see .
Grad A: So . And , uh , another thing would be to extend this to , uh , digits or something where I can look at whole words .
PhD F: Mm - hmm .
Grad A: And I would be able to see , uh , not just , like , phoneme events , but , um , inter - phoneme events . So , like , this is from a stop to to a vo a vocalic
PhD F: Mm - hmm .
Grad A: segment . You know , so something that is transitional in nature .
PhD F: Right .
Grad A: Yeah .
PhD F: Cool . Great .
Grad A: So that 's that 's it .
PhD F: Uh OK .
Grad A: Yeah .
PhD F: Um Let 's see , I haven't done a whole lot on anything related to this this week . I 've been focusing mainly on Meeting Recorder stuff .
PhD C: Oh .
PhD F: So , um , I guess I 'll just pass it on to Dave .
Grad G: Uh , OK . Well , in my lunch talk last week I I said I 'd tried phase normalization and gotten garbage results using that l um , long - term mean subtraction approach . It turned out there was a bug in my Matlab code . So I tried it again , um , and , um , the results were were better . I got intelligible speech back . But they still weren't as good as just subtracting the magnitude the log magnitude means . And also I 've been talking to , um , Andreas and Thilo about the , um , SmartKom language model and about coming up with a good model for , um , far mike use of the SmartKom system . So I 'm gonna be working on , um , implementing this mean subtraction approach in the far - mike system for the SmartKom system , I mean . And , um , one of the experiments we 're gonna do is , um , we 're gonna , um , train the a Broadcast News net , which is because that 's what we 've been using so far , and , um , adapt it on some other data . Um , An - Andreas wants to use , um , data that resembles read speech , like these digit readings , because he feels that the SmartKom system interaction is not gonna be exactly conversational .
PhD F: Mm - hmm .
Grad G: S so actually I was wondering , how long does it take to train that Broadcast News net ?
Professor B: The big one takes a while . Yeah . That takes two , three weeks .
Grad G: Two , three weeks .
Professor B: So but , you know , uh , you can get I don't know if you even want to run the big one , uh , um , in the in the final system , cuz , you know , it takes a little while to run it . So , um , you can scale it down by I 'm sorry , it was two , three weeks for training up for the large Broadcast News test set training set . I don't know how much you 'd be training on .
Grad G: Oh .
Professor B: The full ?
Grad G: OK .
Professor B: Uh , i so if you trained on half as much and made the net , uh , uh , half as big , then it would be one fourth the amount of time
Grad G: OK .
Professor B: and it 'd be nearly as good . So .
Grad G: OK .
Professor B: Yeah . Also , I guess we had we 've had these , uh , little di discussions I guess you ha haven't had a chance to work with it too much about about , uh uh , uh m other ways of taking care of the phase . So , I mean , I I guess that was something I could say would be that we 've talked a little bit about
Grad G: Mm - hmm .
Professor B: you just doing it all with complex arithmetic and , uh and not not , uh , doing the polar representation with magnitude and phase . But it looks like there 's ways that one could potentially just work with the complex numbers and and and in principle get rid of the effects of the average complex spectrum . But Grad G: And , um , actually , regarding the phase normalization So I did two experiments , and one is So , phases get added , modulo two pi , and because you only know the phase of the complex number t t to a value modulo two pi . And so I thought at first , um , that , uh , what I should do is unwrap the phase because that will undo that . Um , but I actually got worse results doing that unwrapping using the simple phase unwrapper that 's in Matlab than I did not unwrapping at all .
PhD D: Mm - hmm .
PhD F: Hmm .
Professor B: Yeah . P So .
Grad G: And that 's all I have to say .
PhD F: Hmm .
Professor B: Yeah . So I 'm I 'm still hopeful that that I mean , we we don't even know if the phase is something the average phase is something that we do want to remove . I mean , maybe there 's some deeper reason why it isn't the right thing to do . But , um , at least in principle it looks like there 's there 's , uh , a couple potential ways to do it . One one being to just work with the complex numbers , um , and , uh in rectangular kind of coordinates . And the other is to , uh , do a Taylor series Well . So you work with the complex numbers and then when you get the spectrum the average complex spectrum um , actually divide it out , um , as opposed to taking the log and subtracting . So then , um , um , you know , there might be some numerical issues . We don't really know that . The other thing we talked a little bit about was Taylor series expansion . And , um , uh , actually I was talking to Dick Karp about it a little bit , and and and , since I got thinking about it , and and , uh , so one thing is that y you 'd have to do , I think , uh we may have to do this on a whiteboard , but I think you have to be a little careful about scaling the numbers that you 're taking the complex numbers that you 're taking the log of because the Taylor expansion for it has , you know , a square and a cube , and and so forth . And and so if  if you have a a number that is modulus , you know , uh , very different from one  It should be right around one , if it 's cuz it 's a expansion of log one one minus epsilon or o is is one plus epsilon , or is it one plus ? Well , there 's an epsilon squared over two and an epsilon cubed over three ,
Grad G: OK .
Professor B: and so forth . So if epsilon is bigger than one , then it diverges .
Grad G: Oh .
Professor B: So you have to do some scaling . But that 's not a big deal cuz it 's the log of  of K times a complex number , then you can just that 's the same as log of K plus log of the complex number .
Grad G: Oh .
Professor B: Uh , so there 's Grad G: OK .
Professor B: converges . But .