PhD F: OK .
Professor B: Uh . Somebody else should run this . I 'm sick of being the one to sort of go through and say , " Well , what do you think about this ? " You wanna ?
PhD D: Yeah .
PhD F: Should we take turns ? You want me to run it today ?
Professor B: Yeah . Why don't you run it today ? OK .
PhD F: OK . OK . Um . Let 's see , maybe we should just get a list of items things that we should talk about . Um , I guess there 's the usual updates , everybody going around and saying , uh , you know , what they 're working on , the things that happened the last week . But aside from that is there anything in particular that anybody wants to bring up
PhD D: Mmm .
PhD F: for today ? No ? OK . So why don't we just around and people can give updates .
PhD E: Oh .
PhD F: Uh , do you want to start , Stephane ?
PhD C: Alright . Um . Well , the first thing maybe is that the p Eurospeech paper is , uh , accepted . Um . Yeah .
PhD F: This is what what do you , uh what 's in the paper there ?
PhD C: So it 's the paper that describe basically the , um , system that were proposed for the Aurora .
PhD F: The one that we s we submitted the last round ?
PhD C: Right , yeah .
PhD D: Yeah .
PhD F: Uh - huh .
PhD C: Um Yeah . So and the , fff {comment} comments seems from the reviewer are good . So .
PhD F: Hmm .
PhD C: Mmm Yeah .
PhD F: Where where 's it gonna be this year ?
PhD C: It 's , uh , Aalborg in Denmark . And it 's ,
PhD F: Oh , OK .
PhD C: yeah , September .
PhD F: Mmm .
PhD C: Mmm Yeah . Then , uh , whhh well , I 've been working on on t mainly on on - line normalization this week . Uh , I 've been trying different slightly slightly different approaches . Um , the first thing is trying to play a little bit again with the , um , time constant . Uh , second thing is , uh , the training of , uh , on - line normalization with two different means , one mean for the silence and one for the speech . Um , and so I have two recursions which are controlled by the , um , probability of the voice activity detector . Mmm . This actually don't s doesn't seem to help , although it doesn't hurt . So . But well , both on - line normalization approach seems equivalent . Well , they PhD F: Are the means pretty different for the two ?
PhD C: Yeah . They can be very different . Yeah . Mm - hmm .
PhD F: Hmm .
Professor B: So do you maybe make errors in different places ? Different kinds of errors ?
PhD C: I didn't look , uh , more closely . Um . It might be , yeah . Mm - hmm . Um . Well , eh , there is one thing that we can observe , is that the mean are more different for for C - zero and C - one than for the other coefficients . And Yeah . And Yeah , it the C - one is There are strange strange thing happening with C - one , is that when you have different kind of noises , the mean for the the silence portion is can be different . And PhD F: Hmm .
PhD C: So when you look at the trajectory of C - one , it 's has a strange shape and I was expecting th the s that these two mean helps , especially because of the the strange C - ze C - one shape , uh , which can like , yo you can have , um , a trajectory for the speech and then when you are in the silence it goes somewhere , but if the noise is different it goes somewhere else .
PhD F: Oh .
PhD C: So which would mean that if we estimate the mean based on all the signal , even though we have frame dropping , but we don't frame ev uh , drop everything , but uh , this can hurts the estimation of the mean for speech , and Mmm . {comment} But I still have to investigate further , I think . Um , a third thing is , um , that instead of t having a fixed time constant , I try to have a time constant that 's smaller at the beginning of the utterances to adapt more quickly to the r something that 's closer to the right mean . T t um Yeah . And then this time constant increases and I have a threshold that Professor B: Mm - hmm .
PhD C: well , if it 's higher than a certain threshold , I keep it to this threshold to still , uh , adapt , um , the mean when  if the utterance is , uh , long enough to to continue to adapt after , like , one second
Professor B: Mm - hmm . Mm - hmm .
PhD C: or Mmm . Uh , well , this doesn't help neither , but this doesn't hurt . So , well . It seems pretty PhD F: Wasn't there some experiment you were gonna try where you did something differently for each , um , uh I don't know whether it was each mel band or each , uh , um , FFT bin or someth There was something you were gonna uh , {comment} some parameter you were gonna vary depending on the frequency . I don't know if that was PhD C: I guess it was I don't know . No . u Maybe it 's this this idea of having different on - line normalization , um , tunings for the different MFCC 's .
PhD F: For each , uh Professor B: Mm - hmm .
PhD C: But Mm - hmm .
PhD F: Yeah . I I thought , Morgan , you brought it up a couple meetings ago . And then it was something about , uh , some and then somebody said " yeah , it does seem like , you know , C - zero is the one that 's , you know , the major one " or , uh , s I can't remember exactly what it was now .
PhD C: Mmm . Yeah . There uh , actually , yeah . S um , it 's very important to normalize C - zero and much less to normalize the other coefficients . And , um , actu uh , well , at least with the current on - line normalization scheme . And we I think , we kind of know that normalizing C - one doesn't help with the current scheme . And and Yeah . In my idea , I I was thinking that the the the reason is maybe because of these funny things that happen between speech and silence which have different means . Um Yeah . But maybe it 's not so  so easy to Professor B: Um , I I really would like to suggest looking , um , a little bit at the kinds of errors . I know you can get lost in that and go forever and not see too much , but  sometimes ,
PhD C: Mm - hmm .
Professor B: but but , um , just seeing that each of these things didn't make things better may not be enough . It may be that they 're making them better in some ways and worse in others ,
PhD C: Yeah . Mm - hmm .
Professor B: or increasing insertions and decreasing deletions , or or , um , um , you know , helping with noisy case but hurting in quiet case . And if you saw that then maybe you it would  something would occur to you of how to deal with that .
PhD C: Mm - hmm . Mm - hmm .
PhD D: Hmm .
PhD C: Alright . Mmm . Yeah . W um , So that 's it , I think , for the on - line normalization . Um Yeah . I 've been playing a little bit with some kind of thresholding , and , mmm , as a first experiment , I think I Yeah . Well , what I did is t is to take , um  to measure the average no , the maximum energy of s each utterance and then put a threshold Well , this for each mel band . Then put a threshold that 's fifteen DB below well , uh , a couple of DB below this maximum ,
Professor B: Mm - hmm . Mmm .
PhD C: and Actually it was not a threshold , it was just adding noise .
Professor B: Mm - hmm .
PhD C: So I was adding a white noise energy , uh , that 's fifteen DB below the maximum energy of the utterance . And Yeah . When we look at at the , um , MFCC that result from this , they are a lot more smoother . Um , when we compare , like , a channel zero and channel one utterance um , so a clean and , uh , the same noisy utterance well , there is almost no difference between the cepstral coefficients of the two .
PhD F: Hmm .
PhD C: Um . And Yeah . And the result that we have in term of speech recognition , actually it 's not it 's not worse , it 's not better neither , but it 's , um , kind of surprising that it 's not worse
PhD F: Hmm .
PhD C: because basically you add noise that 's fifteen DB just fifteen DB below the maximum energy .
Grad A: Sorry .
PhD C: And at least PhD F: So why does that m smooth things out ? I don't I don't understand that .
Professor B: Well , there 's less difference . Right ?
PhD C: It 's I think , it 's whitening This the portion that are more silent ,
Professor B: Cuz it 's PhD C: as you add a white noise that are has a very high energy , it whitens everything
PhD F: Huh . Oh , OK .
PhD C: and and the high - energy portion of the speech don't get much affected anyway by the other noise . And as the noise you add is the same is  the shape , it 's also the same .
PhD F: Hmm .
Professor B: Yeah .
PhD C: So they have the trajectory are very , very similar . And and Professor B: So , I mean , again , if you trained in one kind of noise and tested in the same kind of noise , you 'd you know , given enough training data you don't do b do badly . The reason that we d that we have the problems we have is because it 's different in training and test . Even if the general kind is the same , the exact instances are different . And and
PhD F: Mm - hmm .
Professor B: so when you whiten it , then it 's like you the the only noise to to first order , the only th noise that you have is white noise and you 've added the same thing to training and test .
PhD F: Mm - hmm .
Professor B: So it 's ,
PhD F: Hmm .
Professor B: uh PhD F: So would that be similar to , like , doing the smoothing , then , over time or ?
PhD C: Mm - hmm .
Professor B: Well , it 's a kind of smoothing ,
PhD C: I think it 's I think it 's different .
Professor B: but PhD C: It 's it 's something that yeah , that affects more or less the silence portions because PhD F: Mm - hmm .
PhD 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 .
Professor B: Mm - hmm .
PhD 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 . They are very different when energy 's lower , like during fricatives or during speech pauses . And ,
Professor B: Yeah , but you 're still getting more recognition errors ,
PhD C: uh Professor B: which means that the differences , even though they look like they 're not so big , are are hurting your recognition .
PhD C: Ye
Professor B: Right ?
PhD C: Yeah . So it distort the speech . Right .
Professor B: Yeah .
PhD C: Um .
PhD F: So performance went down ?
PhD C: No . It didn't . But PhD F: Oh .
PhD C: Yeah . So , but in this case I I really expect that maybe the the two these two stream of features , they are very different . I mean , and maybe we could gain something by combining them
Professor B: Well , the other thing is that you just picked one particular way of doing it .
PhD C: or Professor B: Uh , I mean , first place it 's fifteen DB , uh , down across the utterance . And maybe you 'd want to have something that was a little more adaptive . Secondly , you happened to pick fifteen DB
PhD C: Mmm .
Professor B: and maybe twenty 'd be better ,
PhD C: Yeah .
Professor B: or or twelve .
PhD C: Yeah . Right .
PhD F: So what was the what was the threshold part of it ? Was the threshold , uh , how far down ?
Professor B: Yeah . Well , he yeah , he had to figure out how much to add . So he was looking he was looking at the peak value .
PhD F: Uh - huh .
Professor B: Right ? And then PhD C: Uh - huh .
PhD F: And and so what 's ho I don't understand . How does it go ? If it if if the peak value 's above some threshold , then you add the noise ? Or if it 's below s
PhD C: I systematically {comment} add the noise , but the , um , noise level is just some kind of threshold below the peak .
PhD F: Oh , oh . I see .
PhD C: Mmm .
PhD F: I see .
Professor B: Yeah .
PhD C: Um . Yeah . Which is not really noise , actually . It 's just adding a constant to each of the mel , uh , energy .
PhD F: Mm - hmm .
PhD C: To each of the mel filter bank . Yeah .
PhD F: I see .
PhD C: So , yeah , it 's really , uh , white noise . I th
Professor B: Yeah .
PhD F: Mm - hmm .
Professor B: So then afterwards a log is taken , and that 's so sort of why the  the little variation tends to go away .
PhD C: Mm - hmm . Um . Yeah . So may Well , the this threshold is still a factor that we have to look at . And I don't know , maybe a constant noise addition would  would be fine also , or Um Professor B: Or or not constant but but , uh , varying over time in fact is another way to go .
PhD C: Mm - hmm . Mm - hmm .
Professor B: Um .
PhD C: Yeah . Um Professor B: Were you using the the normalization in addition to this ? I mean , what was the rest of the system ?
PhD C: Um Yeah . It was it was , uh , the same system . Mm - hmm .
Professor B: OK .
PhD C: It was the same system . Mmm . Oh , yeah . A third thing is that , um , I play a little bit with the , um  finding what was different between , um , And there were a couple of differences , like the LDA filters were not the same . Um , he had the France Telecom blind equalization in the system . Um , the number o of MFCC that was were used was different . You used thirteen and we used fifteen . Well , a bunch of differences . And , um , actually the result that he he got were much better on TI - digits especially . So I 'm kind of investigated to see what was the main factor for this difference . And it seems that the LDA filter is is was hurting . 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 . And it makes a big difference , um , on TI - digits trained on clean . 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 , um , on noisy speech when the system is trained on clean speech . But and when we use the filter that 's derived from clean speech we jumped so from eighty - two point seven to eighty - five point one , which is a huge leap .
Professor B: Mm - hmm .
PhD C: Um . Yeah . So now the results are more similar , and I don't I will not , I think , investigate on the other differences , which is like the number of MFCC that we keep and other small things that we can I think optimize later on anyway .
Professor B: Sure . 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 . Right ? So if there 's any particular reason to ha pick one or the other , I mean Which which one is closer to what the proposal was that was submitted to Aurora ? Are they they both ? Well , I mean PhD C: I think Yeah . I think th th uh , the new system that I tested is , I guess , closer because it doesn't have it have less of of France Telecom stuff ,
PhD D: You mean the PhD C: I PhD D: The whatever you , uh , tested with recently . Right ?
PhD C: Mmm ? Yeah .
PhD D: Yeah ?
Professor B: Well , no , I I 'm I Yeah , you 're trying to add in France Telecom .
PhD C: But , we Professor B: Tell them about the rest of it . Like you said the number of filters might be different or something . Right ? Or PhD D: The number of cepstral coefficients is what ?
Professor B: Cep
PhD C: Mm - hmm .
Professor B: Yeah . So , I mean , I think we 'd wanna standardize there , wouldn't we ?
PhD C: Yeah , yeah .
Professor B: So , sh you guys should pick something
PhD D: Yeah .
Professor B: and Well , all th all three of you .
PhD D: Yeah .
PhD C: I think we were gonna work with with this or this new system , or with PhD D: Uh , so the the right now , the the system that is there in the what we have in the repositories , with uses fifteen .
PhD C: So Right . Yeah .
PhD D: Yeah , so Yeah , so Yep .
PhD C: But we will use the the LDA filters f derived from clean speech . Well , yeah , actually it 's it 's not the the LDA filter .
PhD D: Yeah , yeah . So PhD C: It 's something that 's also short enough in in latency .
PhD D: Yeah . Well .
PhD C: So .
PhD D: Yeah . So , we haven't w we have been always using , uh , fifteen coefficients ,
PhD C: Yeah .
PhD D: not thirteen ?
PhD C: Mm - hmm .
PhD D: Yeah . Well , uh , that 's something 's Um . Yeah . Then Professor B: I think as long as you guys agree on it , it doesn't matter .
PhD D: mmm Professor B: I think we have a maximum of sixty , uh , features that we 're allowed . So .
PhD C: Yeah .
PhD D: Yeah . Ma - maybe we can I mean , at least , 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 .
PhD C: Mm - hmm . Mm - hmm .
PhD D: Never tested it with the compensation , but without , uh , compensation it was like fifteen was s slightly better than thirteen ,
PhD C: Yeah .
PhD D: so that 's why we stuck to thirteen .
PhD C: Yeah . And there is there is also this log energy versus C - zero .
PhD D: Sorry , fifteen . Yeah , the log energy versus C - zero .
PhD C: Well . W w if if PhD D: Uh , that 's that 's the other thing . I mean , without noise compensation certainly C - zero is better than log energy . Be - I mean , because the there are more , uh , mismatched conditions than the matching conditions for testing .
PhD C: Mm - hmm .
PhD D: You know , always for the matched condition , you always get a slightly better performance for log energy than C - zero .
PhD C: Mm - hmm .
PhD D: But not for I mean , for matched and the clean condition both , you get log energy I mean you get a better performance with log energy .
PhD C: Mm - hmm .
PhD 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 .
PhD C: Mm - hmm .
PhD D: We can see that .
PhD C: Yeah .
PhD D: Hmm .
PhD C: Mmm .
PhD F: So do you have more , Stephane , or ?