B: and you 've added the same thing to training and test .
F: Mm - hmm .
B: So it 's ,
F: Hmm .
B: uh F: So would that be similar to , like , doing the smoothing , then , over time
F: or ?
C: Mm - hmm .
B: Well , it 's a kind of smoothing ,
C: I think it 's I think it 's different .
B: but C: It 's it 's something that yeah , that affects more or less the silence portions
C: because F: Mm - hmm .
C: Well , anyway , the sp the portion of speech that ha have high energy are not ch a lot affected by the noises in the Aurora database .
B: Mm - hmm .
C: If if you compare th the two shut channels of SpeechDat - Car during speech portion , it 's n n n the MFCC are not very different .
C: They are very different when energy 's lower ,
C: like during fricatives or during speech pauses .
C: And ,
C: uh B: Yeah , but you 're still getting more recognition errors ,
B: which means that the differences , even though they look like they 're not so big , are are hurting your recognition .
C: Ye
B: Right ?
C: Yeah . So it distort the speech .
C: Right .
C: Um .
B: Yeah .
F: So performance went down ?
C: No .
C: It didn't .
F: Oh .
C: But C: Yeah .
C: So , but in this case I I really expect that maybe the the two these two stream of features , they are very different .
C: I mean , and maybe we could gain something by combining them
B: Well , the other thing is that you just picked one particular way of doing it .
C: or B: Uh , I mean , first place it 's fifteen DB , uh , down across the utterance .
B: And maybe you 'd want to have something that was a little more adaptive .
B: Secondly , you happened to pick fifteen DB
C: Mmm .
B: and maybe twenty 'd be better ,
C: Yeah .
B: or or twelve .
C: Yeah . Right .
F: So what was the what was the threshold part of it ?
F: Was the threshold , uh , how far down ?
B: Yeah .
B: Well , he yeah , he had to figure out how much to add .
B: So he was looking he was looking at the peak value .
F: Uh - huh .
B: Right ?
C: Uh - huh .
B: And then F: And and so what 's F: ho I don't understand .
F: How does it go ?
F: If it if if the peak value 's above some threshold , then you add the noise ?
F: Or if it 's below s
C: I systematically <comment> add the noise ,
C: but the , um , noise level is just some kind of threshold below the peak .
F: Oh , oh . I see .
C: Mmm .
F: I see .
C: Um .
B: Yeah .
C: Yeah .
C: Which is not really noise , actually .
C: It 's just adding a constant to each of the mel , uh , energy .
F: Mm - hmm .
C: To each of the mel filter bank .
C: Yeah .
F: I see .
C: So , yeah , it 's really , uh , white noise .
C: I th
F: Mm - hmm .
B: Yeah .
B: So then afterwards a log is taken ,
B: and that 's so sort of why the  the little variation tends to go away .
C: Mm - hmm .
C: Um .
C: Yeah . So may
C: Well , the this threshold is still a factor that we have to look at .
C: And I don't know , maybe a constant noise addition would  would be fine also ,
C: or C: Um B: Or or not constant but but , uh , varying over time in fact is another way to go .
C: Mm - hmm .
C: Mm - hmm .
B: Um .
C: Yeah .
C: Um B: Were you using the the normalization in addition to this ?
B: I mean , what was the rest of the system ?
C: Um C: Yeah . It was it was , uh , the same system .
C: Mm - hmm .
B: OK .
C: It was the same system .
C: Mmm .
C: Oh , yeah .
C: A third thing is that , um , I play a little bit with the , um  finding what was different between , um ,
C: And there were a couple of differences ,
C: like the LDA filters were not the same .
C: Um ,
C: he had the France Telecom blind equalization in the system .
C: Um ,
C: the number o of MFCC that was were used was different .
C: You used thirteen
C: and we used fifteen .
C: Well , a bunch of differences .
C: And , um , actually the result that he he got were much better on TI - digits especially .
C: So I 'm kind of investigated to see what was the main factor for this difference .
C: And it seems that the LDA filter is is was hurting .
C: Um , so when we put s some noise compensation the , um , LDA filter that that 's derived from noisy speech is not more anymore optimal .
C: And it makes a big difference , um , on TI - digits
C: trained on clean .
C: Uh , if we use the the old LDA filter , I mean the LDA filter that was in the proposal , we have , like , eighty - two point seven percent recognition rate ,
C: um ,
C: on noisy speech when the system is trained on clean speech .
C: But C: and when we use the filter that 's derived from clean speech we jumped C: so from eighty - two point seven to eighty - five point one ,
C: which is a huge leap .
B: Mm - hmm .
C: Um .
C: Yeah .
C: So now the results are more similar ,
C: and
C: I don't I will not , I think , investigate on the other differences ,
C: which is like the number of MFCC that we keep and other small things
C: that we can I think optimize later on anyway .
B: Sure .
B: But on the other hand if everybody is trying different kinds of noise suppression things and so forth , it might be good to standardize on the piece that we 're not changing .
B: Right ?
B: So if there 's any particular reason to ha pick one or the other ,
B: I mean B: Which which one is closer to what the proposal was that was submitted to Aurora ?
B: Are they B: they both ?
B: Well , I mean C: I think C: Yeah . I think th th uh , the new system that I tested is , I guess , closer
C: because it doesn't have it have less of of France Telecom stuff ,
D: You mean the C: I D: The whatever you , uh , tested with recently . Right ?
C: Mmm ?
C: Yeah .
D: Yeah ?
B: Well , no , I I 'm I B: Yeah , you 're trying to add in France Telecom .
C: But , we B: Tell them about the rest of it .
B: Like you said the number of filters might be different or something .
B: Right ?
D: The number of cepstral coefficients is what ?
B: Or B: Cep
C: Mm - hmm .
B: Yeah .
B: So , I mean , I think we 'd wanna standardize there ,
B: wouldn't we ?
C: Yeah , yeah .
B: So , sh you guys should pick something
D: Yeah .
B: and D: Yeah .
B: Well , all th all three of you .
C: I think we were gonna work with with this or this new system ,
C: or with D: Uh , so the the right now , the the system that is there in the what we have in the repositories , with uses fifteen .
C: So C: Right .
C: Yeah .
D: Yeah ,
D: so Yeah ,
D: so Yep .
C: But we will use the the LDA filters f derived from clean speech .
D: Yeah , yeah .
C: Well , yeah , actually it 's it 's not the the LDA filter .
D: So C: It 's something that 's also short enough in in latency .
D: Yeah .
D: Well .
C: So .
D: Yeah .
D: So , we haven't w we have been always using , uh , fifteen coefficients ,
D: not thirteen ?
C: Yeah .
C: Mm - hmm .
D: Yeah .
D: Well , uh , that 's something 's D: Um .
D: Yeah .
D: Then D: mmm B: I think as long as you guys agree on it , it doesn't matter .
B: I think we have a maximum of sixty , uh , features that we 're allowed .
B: So .
C: Yeah .
D: Yeah . Ma - maybe we can I mean , at least ,
D: um , I 'll t s run some experiments to see whether once I have this <comment> noise compensation to see whether thirteen and fifteen really matters or not .
C: Mm - hmm .
C: Mm - hmm .
D: Never tested it with the compensation ,
D: but without , uh , compensation it was like fifteen was s slightly better than thirteen ,
C: Yeah .
D: so that 's why we stuck to thirteen .
C: Yeah .
C: And there is there is also this log energy versus C - zero .
D: Sorry ,
D: fifteen .
D: Yeah , the log energy versus C - zero .
C: Well .
D: Uh , that 's that 's the other thing .
C: W w if if D: I mean , without noise compensation certainly C - zero is better than log energy .
D: Be - I mean , because the there are more , uh , mismatched conditions than the matching conditions for testing .
C: Mm - hmm .
D: You know ,
D: always for the matched condition , you always get a slightly better performance for log energy than C - zero .
C: Mm - hmm .
D: But not for D: I mean , for matched and the clean condition both , you get log energy D: I mean you get a better performance with log energy .
C: Mm - hmm .
D: Well , um , maybe once we have this noise compensation , I don't know , we have to try that also , whether we want to go for C - zero or log energy .
C: Mm - hmm .
D: We can see that .
C: Yeah .
D: Hmm .
C: Mmm .
F: So do you have more , Stephane ,
F: or ?
C: Uh , that 's it , I think .
C: Mmm .
F: Do you have anything , Morgan ,
F: or ?
B: Uh ,
B: no . I 'm just , you know , being a manager this week .
B: So .
F: How about you , Barry ?
A: Um , still working on my my quals preparation stuff .
A: 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 .
A: So , for example , um , if I know where voicing occurs and everything , um , I would do a phone um , phone recognition experiment ,
A: um , somehow putting in the the , uh the perfect knowledge that I have about voicing .
A: 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 .
A: So say , like , I know this particular segment is voicing ,
F: Mm - hmm .
A: 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 ,
F: Mm - hmm .
A: and then see what kinds of improvements I get .
F: Hmm .
A: 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 .
F: Mm - hmm .
A: So , that 's A: I hope to get those , uh those experiments done by by the time quals come come around in July .
F: So do you just take the probabilities of the other ones and spread them out evenly among the the remaining ones ?
A: Yeah .
A: I I I was thinking A: OK , so just set to set to some really low number , the the non - voiced , um , phones .
F: Mm - hmm .
A: Right ?
A: And then renormalize .
F: Mmm .
A: Right .
F: Cool .
D: Mm - hmm .
A: Yeah .
F: That will be really interesting to see ,
F: you know .
F: So then you 're gonna feed the those into some standard recognizer .
A: Mm - hmm .
F: Uh , wh are you gonna do digits
A: Yeah ,
A: m
F: or ?
A: Um , well , I 'm gonna f work with TIMIT F: With TIMIT . OK .
A: TIMIT uh , phone recognition with TIMIT .
F: Mm - hmm .
A: And , um F: Oh , so then you 'll feed those F: Sorry .
F: So where do the outputs of the net go into if you 're doing phone recognition ?
A: Oh .
A: Um , the outputs of the net go into the standard , h um , ICSI hybrid , um , recognizer .
A: So maybe , um , Chronos
F: An - and you 're gonna the you 're gonna do phone recognition with that ?
A: or A: Phone recognition .
F: OK , OK .
A: Right , right .
F: I see .
A: So .
A: And , uh , another thing would be to extend this to , uh , digits or something where I can look at whole words .
F: Mm - hmm .
A: And I would be able to see , uh , not just , like , phoneme events , but , um , inter - phoneme events .
F: Mm - hmm .
A: So , like , this is from a stop to to a vo a vocalic
A: segment .
A: You know ,
A: so something that is transitional in nature .
F: Right .
F: Cool .
A: Yeah .
F: Great .
F: Uh A: So that 's that 's it .
F: OK .
F: Um A: Yeah .
F: Let 's see , I haven't done a whole lot on anything related to this this week .
F: I 've been focusing mainly on Meeting Recorder stuff .
C: Oh .
F: So , um , I guess I 'll just pass it on to Dave .
G: Uh , OK .
G: 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 .
G: It turned out there was a bug in my Matlab code .
G: So I tried it again ,
G: um ,
G: and , um , the results were were better .
G: I got intelligible speech back .
G: But they still weren't as good as just subtracting the magnitude the log magnitude means .
G: And also I 've been talking to , um , Andreas and Thilo about the , um , SmartKom language model
G: and about coming up with a good model for , um , far mike use of the SmartKom system .
G: So
G: I 'm gonna be working on , um , implementing this mean subtraction approach in the far - mike system G: for the SmartKom system , I mean .
G: And , um ,
G: one of the experiments we 're gonna do is , um , we 're gonna , um , train the a Broadcast News net ,
G: which is because that 's what we 've been using so far ,
G: and , um , adapt it on some other data . Um , An - Andreas wants to use ,
G: um ,
G: data that resembles read speech ,
G: like these digit readings ,
G: because he feels that the SmartKom system interaction is not gonna be exactly conversational .
F: Mm - hmm .
G: S so actually I was wondering , how long does it take to train that Broadcast News net ?
B: The big one takes a while .
B: Yeah . That takes two , three weeks .
G: Two , three weeks .
B: So but , you know , uh , you can get B: I don't know if you even want to run the big one , uh , um , in the in the final system ,
B: cuz , you know , it takes a little while to run it .
B: So , um , you can scale it down by B: I 'm sorry ,
B: it was two , three weeks for training up for the large Broadcast News test set training set .
G: Oh .
B: I don't know how much you 'd be training on .
G: OK .
B: The full ?
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
G: OK .
B: and it 'd be nearly as good .
B: So .
G: OK .
B: Yeah .
B: Also , I guess we had we 've had these , uh , little di discussions B: I guess you ha haven't had a chance to work with it too much B: about about , uh uh , uh m other ways of taking care of the phase .
G: Mm - hmm .
B: So , I mean , I I guess that was something I could say would be that we 've talked a little bit about
B: you just doing it all with complex arithmetic
B: and , uh B: and not not , uh , doing the polar representation with magnitude and phase .
B: 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 .
B: But G: And , um ,
G: actually , regarding the phase normalization G: So I did two experiments ,
G: and one is G: So , phases get added , modulo two pi ,
G: and because you only know the phase of the complex number t t to a value modulo two pi .
G: And so I thought at first , um , that , uh , what I should do is unwrap the phase
G: because that will undo that .
G: 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 .
F: Hmm .
D: Mm - hmm .
B: Yeah .
B: P So .
G: And that 's all I have to say .
F: Hmm .
B: Yeah .
B: So I 'm I 'm still hopeful that that B: I mean , we we don't even know if the phase is something the average phase is something that we do want to remove .
B: I mean , maybe there 's some deeper reason why it isn't the right thing to do .
B: But , um ,
B: at least in principle it looks like there 's there 's , uh , a couple potential ways to do it .
B: One one being to just work with the complex numbers ,
B: um ,
B: and , uh in rectangular kind of coordinates .
B: And the other is to , uh , do a Taylor series B: Well .
B: So you work with the complex numbers
B: and then when you get the spectrum the average complex spectrum um , actually divide it out ,
B: um , as opposed to taking the log and subtracting .
B: So then ,
B: um ,
B: um ,
B: you know , there might be some numerical issues .
B: We don't really know that .
B: The other thing we talked a little bit about was Taylor series expansion .
B: And , um ,
B: uh , actually I was talking to Dick Karp about it a little bit ,
B: and and and , since I got thinking about it ,
B: and and , uh ,
B: so one thing is that y you 'd have to do , I think ,
B: uh B: we may have to do this on a whiteboard ,
B: 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
B: because the Taylor expansion for it has , you know , a square and a cube , and and so forth .
B: 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 ,