PhD E: Mm - hmm .
Professor B: But , um . Well , I don't know . What what do you , uh what do you guys see as as being what you would be doing in the next week , given wha what 's happened ?
PhD C: Cure the VAD ?
PhD E: Yeah .
PhD A: What was that ?
PhD C: VAD .
PhD A: Oh .
PhD C: And Professor B: OK .
PhD E: So , should we keep the same ? I think we might try to keep the same idea of having a neural network , but training it on more data and adding better features , I think , but because the current network is just PLP features . Well , it 's trained on noisy PLP PhD C: Just the cepstra . Yeah .
PhD E: PLP features computed on noisy speech . But  there is no nothing particularly robust in these features .
PhD A: So , I I uh PhD C: No .
PhD E: There 's no RASTA , no PhD A: So , uh , I I don't remember what you said the answer to my , uh , question earlier . Will you will you train the net on after you 've done the spectral subtraction or the Wiener filtering ?
Professor B: This is a different net .
PhD A: Oh .
PhD C: So we have a VAD which is like neur that 's a neural net .
PhD E: Oh , yeah . Hmm .
PhD A: Oh , you 're talking about the VAD net . OK .
PhD C: Yeah .
PhD E: Mm - hmm .
PhD A: I see .
PhD C: So that that VAD was trained on the noisy features .
PhD A: Mm - hmm .
PhD C: So , right now we have , like , uh we have the cleaned - up features , so we can have a better VAD by training the net on the cleaned - up speech .
PhD A: Mm - hmm . I see . I see .
PhD C: Yeah , but we need a VAD for uh noise estimation also . So it 's , like , where do we want to put the VAD ? Uh , it 's like PhD A: Can you use the same net to do both , or ?
PhD C: For PhD A: Can you use the same net that you that I was talking about to do the VAD ?
PhD C: Mm - hmm . Uh , it actually comes at v at the very end .
PhD A: Mm - hmm .
PhD C: So the net the final net I mean , which is the feature net so that actually comes after a chain of , like , LDA plus everything . So it 's , like , it takes a long time to get a decision out of it . And  and you can actually do it for final frame - dropping , but not for the VA - f noise estimation .
PhD A: Mm - hmm .
Professor B: You see , the idea is that the , um , initial decision to that that you 're in silence or speech happens pretty quickly .
PhD A: Oh , OK .
PhD C: Hmm .
PhD A: Cuz that 's used by some of these other ?
Professor B: And that Yeah . And that 's sort of fed forward , and and you say " well , flush everything , it 's not speech anymore " .
PhD A: Oh , OK . I see .
PhD C: Yeah .
PhD A: I thought that was only used for doing frame - dropping later on .
Professor B: Um , it is used , uh Yeah , it 's only used f Well , it 's used for frame - dropping . Um , it 's used for end of utterance
PhD E: Mmm .
Professor B: because , you know , there 's  if you have more than five hundred milliseconds of of of nonspeech then you figure it 's end of utterance or something like that .
PhD A: Mm - hmm .
Professor B: So , um .
PhD E: And it seems important for , like , the on - line normalization . Um . We don't want to update the mean and variance during silen long silence portions . Um . So it it has to be done before
PhD A: Oh . I see .
PhD E: this mean and variance normalization . Um .
Professor B: Um . Yeah . So probably the VAD and and maybe testing out the noise estimation a little bit . I mean , keeping the same method but but , uh , seeing if you cou but , um noise estimation could be improved . Those are sort of related issues .
PhD E: Mm - hmm .
Professor B: It probably makes sense to move from there . And then , uh , later on in the month I think we wanna start including the neural net at the end . Um . OK . Anything else ?
PhD E: The Half Dome was great .
Professor B: Good . Yeah . You didn't didn't fall . That 's good .
PhD C: Well , yeah .
Professor B: Our e our effort would have been devastated if you guys had {comment} run into problems .
PhD A: So , Hynek is coming back next week , you said ?
Professor B: Yeah , that 's the plan .
PhD A: Hmm .
Professor B: I guess the week after he 'll be , uh , going back to Europe , and so we wanna PhD A: Is he in Europe right now or is he up at ?
Professor B: No , no . He 's he 's he 's dropped into the US . Yeah . Yeah .
PhD A: Oh . Hmm .
Professor B: So . Uh . So , uh . Uh , the idea was that , uh , we 'd we 'd sort out where we were going next with this with this work before he , uh , left on this next trip . Good .  Uh , Barry , you just got through your quals , so I don't know if you have much to say . But , uh .
Grad D: Mmm . No , just , uh , looking into some some of the things that , um , uh , John Ohala and Hynek , um , gave as feedback , um , as as a starting point for the project . Um . In in my proposal , I I was thinking about starting from a set of , uh , phonological features , or a subset of them . Um , but that might not be necessarily a good idea according to , um , John .
PhD A: Mm - hmm .
Grad D: He said , uh , um , these these phonological features are are sort of figments of imagination also .
PhD A: Mm - hmm .
Grad D: Um . S
Professor B: In conversational speech in particular . I think you can you can put them in pretty reliably in synthetic speech .
Grad D: Ye
Professor B: But we don't have too much trouble recognizing synthetic speech since we create it in the first place . So , it 's Grad D: Right . Yeah . So , um , a better way would be something more more data - driven ,
PhD A: Mm - hmm .
Grad D: just looking at the data and seeing what 's similar and what 's not similar .
PhD A: Mm - hmm .
Grad D: So , I 'm I 'm , um , taking a look at some of , um , Sangita 's work on on TRAPS . She did something where , um  w where the TRAPS learn She clustered the the temporal patterns of , um , certain certain phonemes in in m averaged over many , many contexts . And , uh , some things tended to cluster .
PhD A: Mm - hmm .
Grad D: Right ? You know , like stop stop consonants clustered really well .
PhD A: Hmm .
Grad D: Um , silence was by its own self .
PhD A: Mm - hmm .
Grad D: And , uh , um , v vocalic was clustered .
PhD A: Mm - hmm .
Grad D: And , um , so , those are interesting things to PhD A: So you 're now you 're sort of looking to try to gather a set of these types of features ?
Grad D: Right .
PhD A: Mm - hmm .
Grad D: Yeah . Just to see where where I could start off from ,
PhD A: Mm - hmm .
Grad D: uh , you know ? A a a set of small features and continue to iterate and find , uh , a better set .
PhD A: Mm - hmm .
Grad D: Yeah .
Professor B: OK . Well , short meeting . That 's OK .
PhD A: Yeah .
Professor B: OK . So next week hopefully we 'll can get Hynek here to to join us and , uh , uh .
PhD A: Should we do digits ?
Professor B: Digits , digits . OK , now .
PhD A: Go ahead , Morgan . You can start .
Professor B: Alright . Let me get my glasses on so I can see them . OK .
PhD A: OK . And we 're off .