Meeting transcript:
speaker: F
text: OK .
speaker: B
text: Uh .
speaker: B
text: Somebody else should run this .
speaker: B
text: I 'm sick of being the one to sort of go through and say , " Well , what do you think about this ? "
speaker: B
text: You wanna <disfmarker> ?
speaker: F
text: Should we take turns ?
speaker: D
text: Yeah .
speaker: F
text: You want me to run it today ?
speaker: B
text: Yeah . Why don't you run it today ?
speaker: F
text: OK .
speaker: B
text: OK .
speaker: F
text: OK .
speaker: F
text: Um .
speaker: F
text: Let 's see , maybe we should just get a list of items <disfmarker>
speaker: F
text: things that we should talk about .
speaker: F
text: Um ,
speaker: F
text: I guess there 's the usual <pause> updates ,
speaker: F
text: everybody going around and saying , uh , you know , what they 're working on ,
speaker: F
text: the things that happened the last week .
speaker: F
text: But aside from that is there anything in particular that anybody wants to bring up
speaker: D
text: Mmm .
speaker: F
text: for today ?
speaker: F
text: No ?
speaker: F
text: OK .
speaker: F
text: So why don't we just around and people can give updates .
speaker: E
text: Oh .
speaker: F
text: Uh , do you want to start , Stephane ?
speaker: C
text: Alright .
speaker: C
text: Um .
speaker: C
text: Well , the first thing maybe is that the p Eurospeech paper is , uh , accepted .
speaker: C
text: Um .
speaker: C
text: Yeah .
speaker: F
text: This is <disfmarker> what <disfmarker> what do you , uh <disfmarker> what 's in the paper there ?
speaker: C
text: So it 's the paper that describe basically the , um , system that were proposed for the <pause> Aurora .
speaker: F
text: The one that we s we submitted the last round ?
speaker: C
text: Right , yeah .
speaker: F
text: Uh - huh .
speaker: D
text: Yeah .
speaker: C
text: Um <disfmarker> Yeah .
speaker: C
text: So and the , fff <comment> comments seems <disfmarker> from the reviewer are good .
speaker: C
text: So .
speaker: F
text: Hmm .
speaker: C
text: Mmm <disfmarker>
speaker: C
text: Yeah .
speaker: F
text: Where <disfmarker> where 's it gonna be this year ?
speaker: C
text: It 's , uh , Aalborg in Denmark .
speaker: F
text: Oh , OK .
speaker: C
text: And it 's ,
speaker: C
text: yeah ,
speaker: C
text: September .
speaker: F
text: Mmm .
speaker: C
text: Mmm <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: Then , uh , whhh
speaker: C
text: well , I 've been working on <disfmarker> on t mainly on on - line normalization this week .
speaker: C
text: Uh , I 've been trying different <disfmarker> slightly <disfmarker> slightly different approaches .
speaker: C
text: Um , the first thing is trying to play a little bit again with the , um , time constant .
speaker: C
text: Uh , second thing is , uh , the training of , uh , on - line normalization with two different means ,
speaker: C
text: one mean for the silence and one for the speech .
speaker: C
text: Um ,
speaker: C
text: and so I have two recursions which are controlled by the , um , probability of the voice activity detector .
speaker: C
text: Mmm .
speaker: C
text: This actually don't s doesn't seem to help ,
speaker: C
text: although it doesn't hurt .
speaker: C
text: So .
speaker: C
text: But <disfmarker> well , both <pause> on - line normalization approach seems equivalent .
speaker: F
text: Are the means pretty different <pause> for the two ?
speaker: C
text: Well , they <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: They can be very different .
speaker: C
text: Yeah . Mm - hmm .
speaker: F
text: Hmm .
speaker: B
text: So do you maybe make errors in different places ?
speaker: B
text: Different kinds of errors ?
speaker: C
text: I didn't look , uh , more closely .
speaker: C
text: Um . It might be , yeah .
speaker: C
text: Mm - hmm .
speaker: C
text: Um .
speaker: C
text: Well , eh , there is one thing that we can observe , is that the mean are more different for <disfmarker> for C - zero and C - one than for the other coefficients .
speaker: C
text: And <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: And <disfmarker> Yeah , it <disfmarker> the C - one is <disfmarker>
speaker: C
text: There are strange <disfmarker> strange thing happening with C - one , is that when you have different kind of noises , the mean for the <disfmarker> the silence portion is <disfmarker> can be different .
speaker: F
text: Hmm .
speaker: C
text: And <disfmarker>
speaker: C
text: So when you look at the trajectory of C - one , it 's <disfmarker> has a strange shape
speaker: C
text: and
speaker: C
text: I was expecting th the s that these two mean helps ,
speaker: C
text: especially because of the <disfmarker> the strange C - ze C - one shape ,
speaker: C
text: uh , which can <disfmarker> like , yo you can have , um , a trajectory for the speech
speaker: C
text: and then when you are in the silence it goes somewhere ,
speaker: C
text: but if the noise is different it goes somewhere else .
speaker: F
text: Oh .
speaker: C
text: 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 ,
speaker: C
text: but <disfmarker> uh , this can <disfmarker> hurts the estimation of the mean for speech ,
speaker: C
text: and <disfmarker>
speaker: C
text: Mmm . <comment> But I still have to investigate further , I think .
speaker: C
text: Um , a third thing is , um , <vocalsound> 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
speaker: C
text: to adapt more quickly to the r something that 's closer to the right mean .
speaker: C
text: T t um <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: And then this time constant increases
speaker: C
text: and I have a threshold that <disfmarker>
speaker: B
text: Mm - hmm .
speaker: C
text: well , if it 's higher than a certain threshold , I keep it to this threshold to still , uh , adapt , um , the mean when <disfmarker> <vocalsound> if the utterance is , uh , long enough to <disfmarker> to continue to adapt after , like , one second
speaker: B
text: Mm - hmm .
speaker: C
text: or <disfmarker>
speaker: B
text: Mm - hmm .
speaker: C
text: Mmm .
speaker: C
text: Uh , well , this doesn't help neither ,
speaker: C
text: but this doesn't hurt .
speaker: C
text: So , well .
speaker: C
text: It seems pretty <disfmarker>
speaker: F
text: Wasn't there some experiment you were gonna try
speaker: F
text: where you did something differently for each , um , <vocalsound> uh <disfmarker> I don't know whether it was each mel band or each , uh , um , FFT bin or someth
speaker: F
text: There was something you were gonna <disfmarker> uh , <comment> some parameter you were gonna vary depending on the frequency .
speaker: F
text: I don't know if that was <disfmarker>
speaker: C
text: I guess it was <disfmarker>
speaker: C
text: I don't know .
speaker: C
text: No .
speaker: C
text: u Maybe it 's this <disfmarker> this idea of having different <pause> on - line normalization , um , tunings for the different MFCC 's .
speaker: F
text: For each , uh <disfmarker>
speaker: B
text: Mm - hmm .
speaker: C
text: But <disfmarker>
speaker: C
text: Mm - hmm .
speaker: F
text: Yeah .
speaker: F
text: I <disfmarker> I thought , Morgan , you brought it up a couple meetings ago .
speaker: F
text: And then it was something about , uh , some
speaker: F
text: 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
speaker: F
text: I can't remember exactly what it was now .
speaker: C
text: Mmm .
speaker: C
text: Yeah . There <disfmarker>
speaker: C
text: uh , actually , yeah .
speaker: C
text: S um , it 's very important to normalize C - zero
speaker: C
text: and <pause> much less to normalize the other coefficients .
speaker: C
text: And , um ,
speaker: C
text: actu
speaker: C
text: uh , well , at least with the current on - line normalization scheme .
speaker: C
text: And
speaker: C
text: we <disfmarker> I think , we <vocalsound> kind of know that normalizing C - one doesn't help with the current scheme .
speaker: C
text: And <disfmarker>
speaker: C
text: and <disfmarker> Yeah .
speaker: C
text: In my idea , I <disfmarker> I was thinking that the <disfmarker> the <disfmarker> the reason is maybe because of these funny things that happen between speech and silence which have different means .
speaker: C
text: Um <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: But maybe it 's not so <disfmarker> <vocalsound> so easy to <disfmarker>
speaker: B
text: Um ,
speaker: B
text: I I really would like to suggest looking , um , a little bit at the kinds of errors .
speaker: B
text: I know you can get lost in that and go forever and not see too much , but <disfmarker> <vocalsound> sometimes ,
speaker: C
text: Mm - hmm .
speaker: B
text: but <disfmarker> but , um ,
speaker: B
text: just seeing that each of these things didn't make things better may not be enough .
speaker: B
text: It may be that they 're making them better in some ways and worse in others ,
speaker: C
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: B
text: or increasing insertions and decreasing deletions ,
speaker: B
text: or <disfmarker>
speaker: B
text: or , um ,
speaker: B
text: um ,
speaker: B
text: you know , helping with noisy case
speaker: B
text: but hurting in quiet case .
speaker: B
text: And if you saw that then maybe you <disfmarker> it would <disfmarker> <vocalsound> something would occur to you of how to deal with that .
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: D
text: Hmm .
speaker: C
text: Alright .
speaker: C
text: Mmm .
speaker: C
text: Yeah .
speaker: C
text: W um ,
speaker: C
text: So that 's it , I think , for the on - line normalization .
speaker: C
text: Um <disfmarker>
speaker: C
text: Yeah . I 've been playing a little bit with some kind of thresholding ,
speaker: C
text: and ,
speaker: C
text: mmm ,
speaker: C
text: as a first experiment , I think I
speaker: C
text: Yeah .
speaker: C
text: Well , what I did is t is to take , um <disfmarker> <vocalsound> to measure the average <disfmarker>
speaker: C
text: no , the maximum energy of s each utterance
speaker: C
text: and then put a threshold <disfmarker>
speaker: C
text: Well , this for each mel band .
speaker: C
text: Then put a threshold that 's fifteen DB below <disfmarker>
speaker: C
text: well , uh , a couple of DB below this maximum ,
speaker: B
text: Mm - hmm .
speaker: B
text: Mmm .
speaker: C
text: and <disfmarker>
speaker: C
text: Actually it was not a threshold ,
speaker: C
text: it was just adding noise .
speaker: B
text: Mm - hmm .
speaker: C
text: So I was adding a white noise energy ,
speaker: C
text: uh , that 's fifteen DB below the maximum energy of the utterance .
speaker: C
text: And <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: When we look at <disfmarker> at the , um , MFCC that result from this , they are <pause> a lot more smoother .
speaker: C
text: Um ,
speaker: C
text: when we compare , like , a channel zero and channel one utterance <disfmarker>
speaker: C
text: um , so a clean and , uh , the same noisy utterance <disfmarker>
speaker: C
text: well , there is almost no difference between the cepstral coefficients of the two .
speaker: F
text: Hmm .
speaker: C
text: Um .
speaker: C
text: And <disfmarker> Yeah .
speaker: C
text: And the result that we have in term of speech recognition , actually it 's not <disfmarker> it 's not worse ,
speaker: C
text: it 's not better neither ,
speaker: F
text: Hmm .
speaker: C
text: but it 's , um , kind of surprising that it 's not worse
speaker: C
text: because basically you add noise that 's fifteen DB <disfmarker> just fifteen DB below <pause> the maximum energy .
speaker: A
text: Sorry .
speaker: C
text: And
speaker: F
text: So why does that m <pause> smooth things out ?
speaker: C
text: at least <disfmarker>
speaker: F
text: I don't <disfmarker> I don't understand that .
speaker: B
text: Well , there 's less difference . Right ?
speaker: C
text: It 's <disfmarker> I think , it 's whitening <disfmarker> This <disfmarker> the portion that are more silent ,
speaker: B
text: Cuz it 's <disfmarker>
speaker: C
text: as you add a white noise that are <disfmarker> has a very high energy , it whitens everything
speaker: F
text: Huh . Oh , OK .
speaker: C
text: and <disfmarker>
speaker: C
text: and the high - energy portion of the speech don't get much affected anyway by the other noise .
speaker: C
text: And as the noise you add is the same is <disfmarker> <pause> the shape , it 's also the same .
speaker: F
text: Hmm .
speaker: B
text: Yeah .
speaker: C
text: So they have <disfmarker> the trajectory are very , very similar .
speaker: C
text: And <disfmarker> and <disfmarker>
speaker: B
text: So , I mean , again , if you trained in one kind of noise and tested in the same kind of noise , you 'd <disfmarker> you know , given enough training data you don't do b do badly .
speaker: B
text: The reason that we d that we have the problems we have is because <pause> it 's different in training and test .
speaker: B
text: Even if <vocalsound> the general kind is the same , the exact instances are different .
speaker: F
text: Mm - hmm .
speaker: B
text: And <disfmarker> and
speaker: B
text: so when you whiten it , then it 's like you <disfmarker> the <disfmarker> the only noise <disfmarker> to <disfmarker> to first order , the only th noise that you have is white noise
speaker: B
text: and you 've added the same thing to training and test .
speaker: F
text: Mm - hmm .
speaker: B
text: So it 's ,
speaker: F
text: Hmm .
speaker: B
text: uh <disfmarker>
speaker: F
text: So would that <pause> be similar to , like , doing the smoothing , then , over time
speaker: F
text: or <disfmarker> ?
speaker: C
text: Mm - hmm .
speaker: B
text: Well , it 's a kind of smoothing ,
speaker: C
text: I think it 's <disfmarker> I think it 's different .
speaker: B
text: but <disfmarker>
speaker: C
text: It 's <disfmarker> it 's something that <disfmarker> yeah , that affects more or less the silence portions
speaker: C
text: because <disfmarker>
speaker: F
text: Mm - hmm .
speaker: C
text: 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 .
speaker: B
text: Mm - hmm .
speaker: C
text: If <disfmarker> 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 .
speaker: C
text: They are very different when energy 's lower ,
speaker: C
text: like during fricatives or during speech pauses .
speaker: C
text: And ,
speaker: C
text: uh <disfmarker>
speaker: B
text: Yeah , but you 're still getting more recognition errors ,
speaker: B
text: which means <vocalsound> that the differences , even though they look like they 're not so big , <vocalsound> are <disfmarker> are hurting your recognition .
speaker: C
text: Ye
speaker: B
text: Right ?
speaker: C
text: Yeah . So it distort <vocalsound> the speech .
speaker: C
text: Right .
speaker: C
text: Um .
speaker: B
text: Yeah .
speaker: F
text: So performance went down ?
speaker: C
text: No .
speaker: C
text: It didn't .
speaker: F
text: Oh .
speaker: C
text: But <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: So , but in this case I <disfmarker> I really expect that maybe the <disfmarker> the two <disfmarker> these two stream of features , they are very different .
speaker: C
text: I mean , and maybe we could gain something by combining them
speaker: B
text: Well , the other thing is that you just picked one particular way of doing it .
speaker: C
text: or <disfmarker>
speaker: B
text: Uh , I mean , first place it 's fifteen DB , uh , <vocalsound> down across the utterance .
speaker: B
text: And <vocalsound> maybe you 'd want to have something that was a little more adaptive .
speaker: B
text: Secondly , you happened to pick fifteen DB
speaker: C
text: Mmm .
speaker: B
text: and maybe twenty 'd be better ,
speaker: C
text: Yeah .
speaker: B
text: or <disfmarker> or twelve .
speaker: C
text: Yeah . Right .
speaker: F
text: So what was the <disfmarker> what was the threshold part of it ?
speaker: F
text: Was the threshold , uh , how far down <disfmarker> ?
speaker: B
text: Yeah .
speaker: B
text: Well , he <disfmarker> yeah , he had to figure out how much to add .
speaker: B
text: So he was looking <disfmarker> he was looking at the peak value .
speaker: F
text: Uh - huh .
speaker: B
text: Right ?
speaker: C
text: Uh - huh .
speaker: B
text: And then <disfmarker>
speaker: F
text: And <disfmarker> and so what 's <disfmarker>
speaker: F
text: ho I don't understand .
speaker: F
text: How does it go ?
speaker: F
text: If it <disfmarker> if <disfmarker> if the peak value 's above some threshold , then you add the noise ?
speaker: F
text: Or if it 's below s
speaker: C
text: I systematically <comment> add the noise ,
speaker: C
text: but the , um , noise level is just <pause> some kind of threshold below the peak .
speaker: F
text: Oh , oh . I see .
speaker: C
text: Mmm .
speaker: F
text: I see .
speaker: C
text: Um .
speaker: B
text: Yeah .
speaker: C
text: Yeah .
speaker: C
text: Which is not really noise , actually .
speaker: C
text: It 's just adding a constant to each of the mel , uh , energy .
speaker: F
text: Mm - hmm .
speaker: C
text: To each of the <pause> mel filter bank .
speaker: C
text: Yeah .
speaker: F
text: I see .
speaker: C
text: So , yeah , it 's really , uh , white noise .
speaker: C
text: I th
speaker: F
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: B
text: So then afterwards a log is taken ,
speaker: B
text: and that 's so sort of why the <disfmarker> <vocalsound> the little variation tends to go away .
speaker: C
text: Mm - hmm .
speaker: C
text: Um .
speaker: C
text: Yeah . So may
speaker: C
text: Well , the <disfmarker> this threshold is still a factor that we have to look at .
speaker: C
text: And I don't know , maybe a constant noise addition would <disfmarker> <vocalsound> would be fine also ,
speaker: C
text: or <disfmarker>
speaker: C
text: Um <disfmarker>
speaker: B
text: Or <disfmarker> or not constant but <disfmarker> but , uh , varying over time <pause> in fact is another way <pause> to go .
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: B
text: Um .
speaker: C
text: Yeah .
speaker: C
text: Um <disfmarker>
speaker: B
text: Were you using the <disfmarker> the normalization in addition to this ?
speaker: B
text: I mean , what was the rest of the system ?
speaker: C
text: Um <disfmarker>
speaker: C
text: Yeah . It was <disfmarker> it was , uh , the same system .
speaker: C
text: Mm - hmm .
speaker: B
text: OK .
speaker: C
text: It was the same system .
speaker: C
text: Mmm .
speaker: C
text: Oh , yeah .
speaker: C
text: A third thing is that , um , <vocalsound> I play a little bit with the , um <disfmarker> <vocalsound> finding what was different between , um ,
speaker: C
text: And there were a couple of differences ,
speaker: C
text: like the LDA filters were not the same .
speaker: C
text: Um ,
speaker: C
text: he had the France Telecom blind equalization in the system .
speaker: C
text: Um ,
speaker: C
text: the number o of MFCC that was <disfmarker> were used was different .
speaker: C
text: You used thirteen
speaker: C
text: and we used fifteen .
speaker: C
text: Well , a bunch of differences .
speaker: C
text: And , um , actually the result that he <disfmarker> he got were much better on TI - digits especially .
speaker: C
text: So I 'm kind of investigated to see what was the main factor for this difference .
speaker: C
text: And it seems that the LDA filter is <disfmarker> is <disfmarker> was hurting .
speaker: C
text: Um , <vocalsound> so when we put s some noise compensation the , um , LDA filter that <disfmarker> that 's derived from noisy speech is not more <disfmarker> anymore optimal .
speaker: C
text: And it makes a big difference , um , <vocalsound> on TI - digits
speaker: C
text: trained on clean .
speaker: C
text: Uh , if we use the <disfmarker> the old LDA filter , I mean the LDA filter that was in the proposal , we have , like , eighty - two point seven percent recognition rate ,
speaker: C
text: um ,
speaker: C
text: on noisy speech when the system is trained on clean speech .
speaker: C
text: But <disfmarker>
speaker: C
text: and when we use the filter that 's derived from clean speech we jumped <disfmarker>
speaker: C
text: so from eighty - two point seven to eighty - five point one ,
speaker: C
text: which is a huge leap .
speaker: B
text: Mm - hmm .
speaker: C
text: Um .
speaker: C
text: Yeah .
speaker: C
text: So now the results are more similar ,
speaker: C
text: and
speaker: C
text: I don't <disfmarker> I will not , I think , investigate on the other differences ,
speaker: C
text: which is like the number of MFCC that we keep and other small things
speaker: C
text: that we can I think optimize later on anyway .
speaker: B
text: Sure .
speaker: B
text: 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 <vocalsound> that we 're not changing .
speaker: B
text: Right ?
speaker: B
text: So if there 's any particular reason to ha pick one or the other ,
speaker: B
text: I mean <disfmarker>
speaker: B
text: Which <disfmarker> which one is closer to what the proposal was that was submitted to Aurora ?
speaker: B
text: Are they <disfmarker>
speaker: B
text: they both <disfmarker> ?
speaker: B
text: Well , I mean <disfmarker>
speaker: C
text: I think <disfmarker>
speaker: C
text: Yeah . I think th th uh , the new system that I tested is , I guess , closer
speaker: C
text: because it doesn't have <disfmarker> it have less of <disfmarker> of France Telecom stuff ,
speaker: D
text: You mean the <disfmarker>
speaker: C
text: I <disfmarker>
speaker: D
text: The <disfmarker> whatever you , uh , tested with recently . Right ?
speaker: C
text: Mmm ?
speaker: C
text: Yeah .
speaker: D
text: Yeah ?
speaker: B
text: Well , no , I <disfmarker> I 'm <disfmarker> I <disfmarker>
speaker: B
text: Yeah , you 're trying to add in France Telecom .
speaker: C
text: But , we <disfmarker>
speaker: B
text: Tell them about the rest of it .
speaker: B
text: Like you said the number of filters might be <vocalsound> different or something .
speaker: B
text: Right ?
speaker: D
text: The number of cepstral coefficients is what ?
speaker: B
text: Or <disfmarker>
speaker: B
text: Cep
speaker: C
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: B
text: So , I mean , I think we 'd wanna standardize there ,
speaker: B
text: wouldn't we ?
speaker: C
text: Yeah , yeah .
speaker: B
text: So , sh you guys should pick something
speaker: D
text: Yeah .
speaker: B
text: and <disfmarker>
speaker: D
text: Yeah .
speaker: B
text: Well , all th all three of you .
speaker: C
text: I think we were gonna work with <disfmarker> with this or this new system ,
speaker: C
text: or with <disfmarker>
speaker: D
text: Uh , so the <disfmarker> the <disfmarker> right now , the <disfmarker> the system that is there in the <disfmarker> what we have in the repositories , with <disfmarker> uses fifteen .
speaker: C
text: So <disfmarker>
speaker: C
text: Right .
speaker: C
text: Yeah .
speaker: D
text: Yeah ,
speaker: D
text: so <disfmarker> Yeah ,
speaker: D
text: so <disfmarker> Yep .
speaker: C
text: But we will use the <disfmarker> the LDA filters f derived from clean speech .
speaker: D
text: Yeah , yeah .
speaker: C
text: Well , yeah , actually it 's <disfmarker> it 's not the <disfmarker> the LDA filter .
speaker: D
text: So <disfmarker>
speaker: C
text: It 's something that 's also short enough in <disfmarker> in latency .
speaker: D
text: Yeah .
speaker: D
text: Well .
speaker: C
text: So .
speaker: D
text: Yeah .
speaker: D
text: So , we haven't <disfmarker> w we have been always using , uh , fifteen coefficients ,
speaker: D
text: not thirteen ?
speaker: C
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: D
text: Yeah .
speaker: D
text: Well , uh , that 's <disfmarker> something 's <disfmarker>
speaker: D
text: Um .
speaker: D
text: Yeah .
speaker: D
text: Then <disfmarker>
speaker: D
text: mmm <disfmarker>
speaker: B
text: I think as long as you guys agree on it , it doesn't matter .
speaker: B
text: I think we have a maximum of sixty , <vocalsound> uh , features that we 're allowed .
speaker: B
text: So .
speaker: C
text: Yeah .
speaker: D
text: Yeah . Ma - maybe we can <disfmarker> I mean , at least ,
speaker: D
text: um , I 'll t s run some experiments to see whether <disfmarker> once I have this <vocalsound> <comment> noise compensation to see whether thirteen and fifteen really matters or not .
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: D
text: Never tested it with the compensation ,
speaker: D
text: but without , <vocalsound> uh , compensation it was like fifteen was s slightly better than thirteen ,
speaker: C
text: Yeah .
speaker: D
text: so that 's why we stuck to thirteen .
speaker: C
text: Yeah .
speaker: C
text: And there is <disfmarker> there is also this log energy versus C - zero .
speaker: D
text: Sorry ,
speaker: D
text: fifteen .
speaker: D
text: Yeah , the log energy versus C - zero .
speaker: C
text: Well .
speaker: D
text: Uh , that 's <disfmarker> that 's the other thing .
speaker: C
text: W w if <disfmarker> if <disfmarker>
speaker: D
text: I mean , without noise compensation certainly C - zero is better than log energy .
speaker: D
text: Be - I mean , because the <disfmarker> there are more , uh , mismatched conditions than the matching conditions for testing .
speaker: C
text: Mm - hmm .
speaker: D
text: You know ,
speaker: D
text: always for the matched condition , you always get a <pause> slightly better performance for log energy than C - zero .
speaker: C
text: Mm - hmm .
speaker: D
text: But not for <disfmarker>
speaker: D
text: I mean , for matched and the clean condition both , you get log energy <disfmarker>
speaker: D
text: I mean you get a better performance with log energy .
speaker: C
text: Mm - hmm .
speaker: D
text: 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 .
speaker: C
text: Mm - hmm .
speaker: D
text: We can see that .
speaker: C
text: Yeah .
speaker: D
text: Hmm .
speaker: C
text: Mmm .
speaker: F
text: So do you have <pause> more , Stephane ,
speaker: F
text: or <disfmarker> ?
speaker: C
text: Uh , that 's it , I think .
speaker: C
text: Mmm .
speaker: F
text: Do you have anything , Morgan ,
speaker: F
text: or <disfmarker> ?
speaker: B
text: Uh ,
speaker: B
text: no . I 'm just , you know , being a manager this week .
speaker: B
text: So .
speaker: F
text: How about you , Barry ?
speaker: A
text: Um , <vocalsound> still working on my <disfmarker> my quals preparation stuff .
speaker: A
text: Um , <vocalsound> so I 'm <disfmarker> I 'm thinking about , um , starting some , <vocalsound> uh , cheating experiments to , uh , determine the , um <disfmarker> <vocalsound> the relative effectiveness of , um , some intermediate categories that I want to classify .
speaker: A
text: So , for example , um , <vocalsound> if I know where voicing occurs and everything , um , <vocalsound> I would do a phone <disfmarker> um , phone recognition experiment ,
speaker: A
text: um , somehow putting in the <disfmarker> the , uh <disfmarker> the perfect knowledge that I have about voicing .
speaker: A
text: So , um , in particular I was thinking , <vocalsound> um , in <disfmarker> in the hybrid framework , just taking those LNA files , <vocalsound> and , um , <vocalsound> setting to zero those probabilities that , um <disfmarker> that these phones are not voicing .
speaker: A
text: So say , like , I know this particular segment is voicing ,
speaker: F
text: Mm - hmm .
speaker: A
text: um , <vocalsound> I would say , uh , go into the corresponding LNA file and zonk out the <disfmarker> the posteriors for , um , those phonemes that , um , are not voiced ,
speaker: F
text: Mm - hmm .
speaker: A
text: and then see what kinds of improvements I get .
speaker: F
text: Hmm .
speaker: A
text: And so this would be a useful thing , um , to know <vocalsound> in terms of , like , which <disfmarker> which , um <disfmarker> which of these categories are <disfmarker> are good for , um , speech recognition .
speaker: F
text: Mm - hmm .
speaker: A
text: So , that 's <disfmarker>
speaker: A
text: I hope to get those , uh <disfmarker> those experiments done by <disfmarker> by the time quals come <disfmarker> come around in July .
speaker: F
text: So do you just take the probabilities of the other ones and spread them out evenly among the <disfmarker> the remaining ones ?
speaker: A
text: Yeah .
speaker: A
text: I <disfmarker> I <disfmarker> I was thinking <disfmarker>
speaker: A
text: OK , so just set to <disfmarker> set to some really low number , the <disfmarker> the non - voiced , um , phones .
speaker: F
text: Mm - hmm .
speaker: A
text: Right ?
speaker: A
text: And then renormalize .
speaker: F
text: Mmm .
speaker: A
text: Right .
speaker: F
text: Cool .
speaker: D
text: Mm - hmm .
speaker: A
text: Yeah .
speaker: F
text: That will be really interesting to see ,
speaker: F
text: you know .
speaker: F
text: So then you 're gonna feed the <disfmarker> those into <pause> some standard recognizer .
speaker: A
text: Mm - hmm .
speaker: F
text: Uh , wh are you gonna do digits
speaker: A
text: Yeah ,
speaker: A
text: m
speaker: F
text: or <disfmarker> ?
speaker: A
text: Um , well , I 'm gonna f work with TIMIT <disfmarker>
speaker: F
text: With TIMIT . OK .
speaker: A
text: TIMIT <disfmarker> uh , phone recognition with TIMIT .
speaker: F
text: Mm - hmm .
speaker: A
text: And , um <disfmarker>
speaker: F
text: Oh , so then you 'll feed those <disfmarker>
speaker: F
text: Sorry .
speaker: F
text: So where do the outputs of the net go into if you 're doing phone recognition ?
speaker: A
text: Oh .
speaker: A
text: Um , the outputs of the net go into the standard , h um , ICSI hybrid , um , recognizer .
speaker: A
text: So maybe , um , Chronos
speaker: F
text: An - and you 're gonna <disfmarker> the <disfmarker> you 're gonna do phone recognition with that ?
speaker: A
text: or <disfmarker>
speaker: A
text: Phone recognition .
speaker: F
text: OK , OK .
speaker: A
text: Right , right .
speaker: F
text: I see .
speaker: A
text: So .
speaker: A
text: And , uh , another thing would be to extend this to , uh , digits or something where I can look at whole words .
speaker: F
text: Mm - hmm .
speaker: A
text: And I would be able to see , uh , not just , like , phoneme events , but , um , <vocalsound> inter - phoneme events .
speaker: F
text: Mm - hmm .
speaker: A
text: So , like , this is from a stop to <disfmarker> to a vo a vocalic
speaker: A
text: segment .
speaker: A
text: You know ,
speaker: A
text: so something that is transitional in nature .
speaker: F
text: Right .
speaker: F
text: Cool .
speaker: A
text: Yeah .
speaker: F
text: Great .
speaker: F
text: Uh <disfmarker>
speaker: A
text: So that 's <disfmarker> that 's it .
speaker: F
text: OK .
speaker: F
text: Um <disfmarker>
speaker: A
text: Yeah .
speaker: F
text: Let 's see , I haven't done a whole lot on anything related to this this week .
speaker: F
text: I 've been focusing mainly on Meeting Recorder stuff .
speaker: C
text: Oh .
speaker: F
text: So , um , <vocalsound> I guess I 'll just pass it on to Dave .
speaker: G
text: Uh , OK .
speaker: G
text: Well , in my lunch talk last week I <disfmarker> I said I 'd tried phase normalization and gotten garbage results using that l um , long - term mean subtraction approach .
speaker: G
text: It turned out there was a bug in my Matlab code .
speaker: G
text: So I tried it again ,
speaker: G
text: um ,
speaker: G
text: and , um , the results <vocalsound> were <disfmarker> were better .
speaker: G
text: I got intelligible speech back .
speaker: G
text: But they still weren't as good as just subtracting the magnitude <disfmarker> the log magnitude means .
speaker: G
text: And also I 've been talking to , um , Andreas and Thilo about the , um , SmartKom language model
speaker: G
text: and about coming up with a good model for , um , far mike use of the SmartKom system .
speaker: G
text: So
speaker: G
text: I 'm gonna be working on , um , implementing this mean subtraction approach in the <vocalsound> far - mike system <disfmarker>
speaker: G
text: for the SmartKom system , I mean .
speaker: G
text: And , um ,
speaker: G
text: one of the experiments we 're gonna do is , um , we 're gonna , um , train the <disfmarker> a Broadcast News net ,
speaker: G
text: which is because that 's what we 've been using so far ,
speaker: G
text: and , um , adapt it on some other data . Um , An - Andreas wants to use ,
speaker: G
text: um ,
speaker: G
text: data that resembles read speech ,
speaker: G
text: like <pause> these digit readings ,
speaker: G
text: because he feels that the SmartKom system interaction is not gonna be exactly conversational .
speaker: F
text: Mm - hmm .
speaker: G
text: S so actually I was wondering , how long does it take to train that Broadcast News net ?
speaker: B
text: The big one takes a while .
speaker: B
text: Yeah . That takes two , three weeks .
speaker: G
text: Two , three weeks .
speaker: B
text: So <disfmarker> but , you know , uh , you can get <disfmarker>
speaker: B
text: I don't know if you even want to run the big one , uh , um , in the <disfmarker> in the final system ,
speaker: B
text: cuz , you know , it takes a little while to run it .
speaker: B
text: So , <vocalsound> um , you can scale it down by <disfmarker>
speaker: B
text: I 'm sorry ,
speaker: B
text: it was two , three weeks for training up for the large Broadcast News test set <disfmarker> training set .
speaker: G
text: Oh .
speaker: B
text: I don't know how much you 'd be training on .
speaker: G
text: OK .
speaker: B
text: The full ?
speaker: B
text: Uh , i so if you trained on half as much <vocalsound> and made the net , uh , uh , half as big , then it would be one fourth <pause> the amount of time
speaker: G
text: OK .
speaker: B
text: and it 'd be nearly as good .
speaker: B
text: So .
speaker: G
text: OK .
speaker: B
text: Yeah .
speaker: B
text: Also , I guess we had <disfmarker> we 've had these , uh , little di discussions <disfmarker>
speaker: B
text: I guess you ha haven't had a chance to work with it too much <disfmarker>
speaker: B
text: about <disfmarker> about , uh <disfmarker> uh , uh m other ways of taking care of the phase .
speaker: G
text: Mm - hmm .
speaker: B
text: So , I mean , I <disfmarker> I guess that was something I could say would be that we 've talked a little bit about
speaker: B
text: you just doing it all with complex arithmetic
speaker: B
text: and , uh <disfmarker>
speaker: B
text: and not <disfmarker> not , uh , doing the polar representation with magnitude and phase .
speaker: B
text: But <vocalsound> it looks like there 's ways that one could potentially just work with the complex numbers and <disfmarker> and <disfmarker> and in principle get rid of the <vocalsound> effects of the average complex spectrum .
speaker: B
text: But <disfmarker>
speaker: G
text: And , um ,
speaker: G
text: actually , regarding the phase normalization <disfmarker>
speaker: G
text: So I did two experiments ,
speaker: G
text: and one is <disfmarker>
speaker: G
text: So , phases get added , modulo two pi ,
speaker: G
text: and <disfmarker> because you only know the phase of the complex number t t to a value modulo two pi .
speaker: G
text: And so I thought at first , um , that , uh , what I should do is unwrap the phase
speaker: G
text: because that will undo that .
speaker: G
text: 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 .
speaker: F
text: Hmm .
speaker: D
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: B
text: P So .
speaker: G
text: And that 's all I have to say .
speaker: F
text: Hmm .
speaker: B
text: Yeah .
speaker: B
text: So I 'm <disfmarker> I 'm still hopeful that <disfmarker> that <disfmarker>
speaker: B
text: I mean , we <disfmarker> we don't even know if the phase <vocalsound> is something <disfmarker> the average phase is something that we do want to remove .
speaker: B
text: I mean , maybe there 's some deeper reason why it isn't the right thing to do .
speaker: B
text: But , um ,
speaker: B
text: at least in principle it looks like there 's <disfmarker> there 's , uh , a couple potential ways to do it .
speaker: B
text: One <disfmarker> one being to just work with the complex numbers ,
speaker: B
text: um ,
speaker: B
text: and , uh <disfmarker> in rectangular kind of coordinates .
speaker: B
text: And the other is <vocalsound> to , uh , do a Taylor series <disfmarker>
speaker: B
text: Well .
speaker: B
text: So you work with the complex numbers
speaker: B
text: and then when you get the spectrum <disfmarker> the average complex spectrum <disfmarker> um , actually divide it out ,
speaker: B
text: um , as opposed to taking the log and subtracting .
speaker: B
text: So then ,
speaker: B
text: um ,
speaker: B
text: um ,
speaker: B
text: you know , there might be some numerical issues .
speaker: B
text: We don't really know that .
speaker: B
text: The other thing we talked a little bit about was Taylor series expansion .
speaker: B
text: And , um ,
speaker: B
text: uh , actually I was talking to Dick Karp about it a little bit ,
speaker: B
text: and <disfmarker> and <disfmarker> and , since I got thinking about it ,
speaker: B
text: and <disfmarker> and , uh ,
speaker: B
text: so one thing is that y you 'd have to do , I think ,
speaker: B
text: uh <disfmarker>
speaker: B
text: we may have to do this on a whiteboard ,
speaker: B
text: but I think you have to be a little careful about scaling the numbers that you 're <vocalsound> taking <disfmarker> the complex numbers that you 're taking the log of
speaker: B
text: because <vocalsound> the Taylor expansion for it has , you know , a square and a cube , and <disfmarker> and so forth .
speaker: B
text: And <disfmarker> and so if <disfmarker> <vocalsound> if you have a <disfmarker> a number that is modulus , you know , uh , very different from one <disfmarker> <vocalsound> It should be right around one ,
speaker: B
text: if it 's <disfmarker>
speaker: B
text: cuz it 's a expansion of log one <disfmarker>
speaker: B
text: one minus epsilon
speaker: B
text: or o is <disfmarker> is <vocalsound> one plus epsilon ,
speaker: B
text: or is it one plus <disfmarker> ?
speaker: B
text: Well , there 's an epsilon squared over two
speaker: G
text: OK .
speaker: B
text: and an epsilon cubed over three ,
speaker: B
text: and so forth .
speaker: B
text: So if epsilon is bigger than one , then it diverges .
speaker: G
text: Oh .
speaker: B
text: So you have to do some scaling .
speaker: B
text: But that 's not a big deal
speaker: B
text: cuz it 's the log of <disfmarker> <vocalsound> of K times a complex number ,
speaker: B
text: then you can just <disfmarker> that 's the same as log of K plus <vocalsound> log of the complex number .
speaker: G
text: Oh .
speaker: G
text: OK .
speaker: B
text: Uh ,
speaker: B
text: so there 's <disfmarker>
speaker: B
text: converges .
speaker: B
text: But .
speaker: F
text: Hmm .
speaker: F
text: OK .
speaker: F
text: How about you , Sunil ?
speaker: D
text: So , um , I 've been , uh , implementing this , uh , Wiener filtering for this Aurora task .
speaker: D
text: And , uh ,
speaker: D
text: I <disfmarker> I actually thought it was <disfmarker> it was doing fine when I tested it once .
speaker: D
text: I it 's , like , using a small section of the code .
speaker: D
text: And then I ran the whole recognition experiment with Italian
speaker: D
text: and I got , <vocalsound> like , worse results than not using it .
speaker: D
text: Then I <disfmarker>
speaker: D
text: So , I 've been trying to find where the problem came from .
speaker: D
text: And then it looks like I have some problem in the way <disfmarker>
speaker: D
text: there is some <disfmarker> some very silly bug somewhere .
speaker: D
text: And , ugh !
speaker: D
text: I <disfmarker> I mean , i uh , it actually <disfmarker> i it actually made the whole thing worse .
speaker: D
text: I was looking at the spectrograms that I got
speaker: D
text: and it 's , like <disfmarker> w it 's <disfmarker> it 's very horrible .
speaker: D
text: Like , when I <disfmarker>
speaker: B
text: I <disfmarker> I missed the v
speaker: B
text: I 'm sorry ,
speaker: B
text: I was <disfmarker> I was distracted .
speaker: B
text: I missed the very first sentence .
speaker: B
text: So then , I 'm a little lost on the rest .
speaker: D
text: Oh , I mean <disfmarker>
speaker: B
text: What <disfmarker> what <disfmarker> what <disfmarker> ?
speaker: D
text: Oh , yeah .
speaker: D
text: I actually implemented the Wiener f f fil filtering as a module and then tested it out separately .
speaker: B
text: Yeah , I see .
speaker: B
text: Oh , OK .
speaker: D
text: And it <disfmarker> it <disfmarker> it gave , like <disfmarker> I just got the signal out
speaker: D
text: and it <disfmarker> it was OK .
speaker: D
text: So , I plugged it in somewhere
speaker: D
text: and then <disfmarker> I mean , it 's like I had to remove some part
speaker: D
text: and then plugging it in somewhere .
speaker: D
text: And then I <disfmarker> in that process I messed it up somewhere .
speaker: D
text: So .
speaker: B
text: OK .
speaker: D
text: So , it was real
speaker: D
text: I mean , I thought it was all fine
speaker: D
text: and then I ran it , and I got something worse than not using it .
speaker: D
text: So ,
speaker: D
text: I was like <disfmarker> I 'm trying to find where the m m problem came ,
speaker: B
text: Uh - huh .
speaker: D
text: and it seems to be , like , somewhere <disfmarker>
speaker: B
text: OK .
speaker: D
text: some silly stuff .
speaker: D
text: And , um , the other thing , uh , was , uh , uh <disfmarker>
speaker: D
text: Well , Hynek showed up one <disfmarker> suddenly on one day
speaker: D
text: and then I was t talking wi
speaker: B
text: Right .
speaker: B
text: Yeah .
speaker: B
text: As <disfmarker> as he is wont to do .
speaker: B
text: Yeah .
speaker: D
text: Uh , yeah .
speaker: D
text: So I was actually <disfmarker> that day I was thinking about d doing something about the Wiener filtering , and then Carlos matter of stuff .
speaker: D
text: And then he showed up
speaker: D
text: and then I told him .
speaker: D
text: And then he gave me a whole bunch of filters <disfmarker>
speaker: D
text: what Carlos used for his , uh , uh , thesis
speaker: D
text: and then <vocalsound> that was something which came up .
speaker: D
text: And then , um <disfmarker>
speaker: D
text: So , uh , I 'm actually , <vocalsound> uh , thinking of using that also in this , uh , W Wiener filtering
speaker: D
text: because that is a m modified Wiener filtering approach ,
speaker: D
text: where instead of using the current frame , it uses <vocalsound> adjacent frames also in designing the Wiener filter .
speaker: D
text: So instead of designing our own new Wiener filters , I may just use one of those Carlos filters in <disfmarker> in this implementation
speaker: B
text: Mm - hmm .
speaker: D
text: and see whether it <disfmarker> it actually gives me something better
speaker: D
text: than using just the current f current frame ,
speaker: D
text: which is in a way , uh , something like the smoothing <disfmarker> the Wiener filter <disfmarker>
speaker: B
text: Mm - hmm .
speaker: D
text: but @ @ <disfmarker>
speaker: D
text: S so , I don't know ,
speaker: D
text: I was h
speaker: D
text: I 'm <disfmarker> I 'm <disfmarker> I 'm , like <disfmarker>
speaker: D
text: that <disfmarker> so that is the next thing . Once this <disfmarker> I <disfmarker> once I sort this pro uh , problem out maybe I 'll just go into that also .
speaker: D
text: And
speaker: D
text: the <disfmarker> the other thing was about the subspace approach .
speaker: D
text: So , um ,
speaker: D
text: I , like , plugged some groupings for computing this eigen uh , uh , uh , s values and eigenvectors .
speaker: D
text: So just <disfmarker> I just @ @ some small block of things which I needed to put together for the subspace approach .
speaker: D
text: And I 'm in the process of , like , building up that stuff .
speaker: D
text: And , um ,
speaker: D
text: uh <disfmarker> <nonvocalsound> Yeah .
speaker: D
text: I guess <disfmarker> Yep . I guess that 's it .
speaker: D
text: And , uh , th th that 's where I am right now .
speaker: D
text: So .
speaker: F
text: Oh . How about you , Carmen ?
speaker: E
text: Mmm . I 'm working with VTS .
speaker: E
text: Um , I do several experiment with the Spanish database first ,
speaker: E
text: only with VTS and nothing more .
speaker: E
text: Not VAD ,
speaker: E
text: no LDA ,
speaker: E
text: nothing more .
speaker: F
text: What <disfmarker> what is VTS again ?
speaker: D
text: New <disfmarker>
speaker: E
text: Eh , Vectorial Taylor Series .
speaker: F
text: Oh , yes .
speaker: F
text: Right , right .
speaker: E
text: To remove the noise too .
speaker: F
text: I think I ask you that every single meeting ,
speaker: F
text: don't I ?
speaker: E
text: What ?
speaker: F
text: I ask you that question every meeting .
speaker: E
text: Yeah .
speaker: E
text: If <disfmarker> Well <disfmarker>
speaker: B
text: So , that 'd be good from <disfmarker> for analysis .
speaker: B
text: It 's good to have some , uh , cases of the same utterance at different <disfmarker> different times .
speaker: F
text: Yeah .
speaker: F
text: " What is VTS ? "
speaker: B
text: Yeah .
speaker: E
text: VTS .
speaker: E
text: I 'm sor
speaker: E
text: Well , um , the question is that <disfmarker>
speaker: E
text: Well .
speaker: E
text: Remove some noise but not too much .
speaker: E
text: And when we put the <disfmarker> m m the , em , VAD , the result is better .
speaker: E
text: And we put everything , the result is better ,
speaker: E
text: but it 's not better than the result that we have without VTS .
speaker: E
text: No , no .
speaker: B
text: I see .
speaker: B
text: So that @ @ <comment> given that you 're using the VAD also , the effect of the VTS is not <pause> so far <disfmarker>
speaker: E
text: Is not .
speaker: B
text: Do you <disfmarker> How much of that do you think is due to just the particular implementation and how much you 're adjusting it ?
speaker: B
text: Or how much do you think is intrinsic to <disfmarker> ?
speaker: E
text: Pfft . I don't know
speaker: E
text: because <disfmarker>
speaker: E
text: Hhh ,
speaker: C
text: Are you still using only the ten first frame for noise estimation
speaker: C
text: or <disfmarker> ?
speaker: E
text: Uh , I do the experiment using only the f onl eh , to use on only one fair estimation of the noise .
speaker: C
text: Or i ?
speaker: C
text: Yeah .
speaker: C
text: Hmm .
speaker: E
text: And also I did some experiment , <vocalsound> uh , doing , um , a lying estimation of the noise .
speaker: E
text: And , well , it 's a little bit better but not <disfmarker>
speaker: E
text: n
speaker: C
text: Maybe you have to standardize this thing also ,
speaker: C
text: noise estimation ,
speaker: C
text: because all the thing that you are testing use a different <disfmarker>
speaker: E
text: Mmm .
speaker: D
text: Mmm .
speaker: C
text: They all need some <disfmarker> some noise <disfmarker> noise spectra
speaker: E
text: No , I do that two <disfmarker> t did two time .
speaker: C
text: but they use <disfmarker> every <disfmarker> all use a different one .
speaker: B
text: I have an idea .
speaker: B
text: If <disfmarker> if , uh , uh ,
speaker: B
text: y you 're right .
speaker: B
text: I mean , each of these require this .
speaker: B
text: Um , given that we 're going to have for this test at least of <disfmarker> uh , boundaries , what if initially we start off by using <pause> known sections of nonspeech <pause> for the estimation ?
speaker: E
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: B
text: Right ?
speaker: C
text: Yeah .
speaker: B
text: S so , e um ,
speaker: C
text: Mm - hmm .
speaker: B
text: first place , I mean even if ultimately we wouldn't be given the boundaries , <vocalsound> uh , this would be a good initial experiment to separate out the effects of things .
speaker: B
text: I mean , how much is the poor <disfmarker> <vocalsound> you know , relatively , uh , unhelpful result that you 're getting in this or this or this
speaker: B
text: is due to some inherent limitation to the method for these tasks
speaker: B
text: and how much of it is just due to the fact that you 're not accurately <vocalsound> finding enough regions that <disfmarker> that are really <vocalsound> n noise ?
speaker: D
text: Mmm .
speaker: E
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: B
text: Um .
speaker: B
text: So maybe if you tested it using that , <vocalsound> you 'd have more reliable <pause> stretches of nonspeech to do the estimation from
speaker: B
text: and see if that helps .
speaker: E
text: Yeah .
speaker: E
text: Another thing is the , em <disfmarker> the codebook ,
speaker: E
text: the initial codebook .
speaker: E
text: That maybe ,
speaker: E
text: well , it 's too clean
speaker: E
text: and <disfmarker>
speaker: B
text: Mm - hmm .
speaker: E
text: Cuz it 's a <disfmarker>
speaker: E
text: I don't know .
speaker: E
text: The methods <disfmarker>
speaker: E
text: If you want , you c I can say something about the method .
speaker: B
text: Mm - hmm .
speaker: E
text: Yeah . In the <disfmarker>
speaker: E
text: Because it 's <vocalsound> a little bit different of the other method .
speaker: E
text: Well , we have <disfmarker>
speaker: E
text: If this <disfmarker> if this is the noise signal , <nonvocalsound> uh , in the log domain , we have something like this .
speaker: E
text: Now , we have something like this .
speaker: E
text: And the idea of these methods is to <disfmarker> <vocalsound> n given a , um <disfmarker>
speaker: E
text: How do you say ?
speaker: E
text: I will read because it 's better for my English .
speaker: E
text: I i given
speaker: E
text: is the estimate of the PDF of the noise signal
speaker: E
text: when we have a , um , a statistic of the clean speech and an statistic of the noisy speech .
speaker: E
text: And the clean speech <disfmarker> the statistic of the clean speech is <pause> from a <pause> codebook .
speaker: E
text: Mmm ? This is the idea .
speaker: E
text: Well , like , this relation is not linear .
speaker: E
text: The methods propose to develop this in a vectorial Taylor series <pause> approximation .
speaker: B
text: I I 'm actually just confused about <pause> the equations you have up there .
speaker: B
text: So , uh ,
speaker: B
text: the top equation is <disfmarker> is <disfmarker> is <disfmarker>
speaker: E
text: No , this in the <disfmarker> it 's <disfmarker> this is the log domain .
speaker: E
text: I <disfmarker> I must to say that .
speaker: B
text: Which is <disfmarker> which is the log domain ?
speaker: E
text: Is the T <disfmarker> is egual <disfmarker> <comment> is equal to , uh , log of <disfmarker>
speaker: B
text: And <disfmarker>
speaker: B
text: but Y is what ?
speaker: B
text: Y of <disfmarker> the spectrum
speaker: E
text: Uh , this <disfmarker> this is this
speaker: B
text: or <disfmarker> ?
speaker: E
text: and this is this .
speaker: B
text: No , no .
speaker: B
text: The top Y is what ?
speaker: E
text: Mm - hmm .
speaker: B
text: Is that power spectrum ?
speaker: E
text: Uh , this is the noisy speech .
speaker: C
text: p s this <disfmarker>
speaker: B
text: No , is that power spectrum ?
speaker: B
text: Is it <disfmarker> ?
speaker: C
text: Yeah . I guess it 's the power spectrum of noisy speech .
speaker: E
text: Yeah . It 's the power spectrum .
speaker: B
text: Oh , OK .
speaker: C
text: Yeah . And <disfmarker>
speaker: B
text: So that 's uh <disfmarker>
speaker: E
text: This is the noisy <disfmarker>
speaker: E
text: Yeah , it 's <disfmarker>
speaker: E
text: of the value <disfmarker>
speaker: B
text: OK .
speaker: B
text: Yeah ,
speaker: B
text: OK .
speaker: B
text: So this <disfmarker> it 's the magnitude squared or something .
speaker: E
text: Yeah .
speaker: B
text: OK , so you have power spectrum added there
speaker: B
text: and down here you have <disfmarker> <vocalsound> you <disfmarker> you put the <disfmarker>
speaker: B
text: depends on T ,
speaker: E
text: w o
speaker: B
text: but <disfmarker> b all of this is just <disfmarker> you just mean <disfmarker>
speaker: E
text: Yeah . It 's the same .
speaker: B
text: you just mean the log of the <disfmarker> of the one up above .
speaker: E
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: B
text: And , uh , so that is X times ,
speaker: B
text: uh ,
speaker: E
text: Yeah , maybe <disfmarker>
speaker: D
text: One <disfmarker> one plus N by X .
speaker: E
text: But , n
speaker: B
text: o
speaker: E
text: Well , y we can expre we can put this expression <disfmarker>
speaker: B
text: X times one plus , uh , N <disfmarker> uh , N <disfmarker> N <disfmarker> N minus X ?
speaker: E
text: The <disfmarker>
speaker: E
text: Yeah .
speaker: B
text: And then ,
speaker: E
text: And the noise signal .
speaker: B
text: uh <disfmarker> So that 's log of X plus log of one plus , uh <disfmarker>
speaker: B
text: Well .
speaker: B
text: Is that right ?
speaker: B
text: Log of <disfmarker>
speaker: D
text: One plus N by X .
speaker: E
text: Well , mmm <disfmarker>
speaker: B
text: I actually don't see how you get that .
speaker: E
text: Well , if we apply the log , we have E is n
speaker: B
text: Uh .
speaker: C
text: Mmm .
speaker: E
text: uh , log <disfmarker> <nonvocalsound> E is equal , oh , to log of X plus N .
speaker: D
text: Uh , and <disfmarker>
speaker: B
text: Yeah .
speaker: E
text: And , well ,
speaker: D
text: And , log of <disfmarker>
speaker: E
text: uh , we can say that E <nonvocalsound> <vocalsound> is equal to log of , <nonvocalsound> <nonvocalsound> um , exponential of X plus exponential of N .
speaker: B
text: Uh <disfmarker>
speaker: D
text: Mm - hmm .
speaker: B
text: No .
speaker: D
text: No .
speaker: B
text: That doesn't follow .
speaker: D
text: Well , if E restricts <disfmarker>
speaker: D
text: It is y
speaker: E
text: Well , this is <disfmarker> this is in the ti the time domain .
speaker: E
text: Well , we have that ,
speaker: E
text: um <disfmarker>
speaker: E
text: We have first that , for example , X is equal ,
speaker: E
text: uh <disfmarker>
speaker: E
text: Well .
speaker: E
text: This is the frequency domain
speaker: E
text: and we can put <vocalsound> u that n the log domain <disfmarker>
speaker: B
text: Yeah .
speaker: E
text: log of X omega ,
speaker: E
text: but , well , in the time domain we have an exponential .
speaker: E
text: No ?
speaker: E
text: No ?
speaker: E
text: Oh , maybe it 's I am <disfmarker>
speaker: E
text: I 'm problem .
speaker: B
text: Yeah .
speaker: B
text: I mean , just never mind what they are .
speaker: B
text: Uh , it 's just if X and N are variables <disfmarker>
speaker: B
text: Right ?
speaker: D
text: What is , uh <disfmarker> ?
speaker: B
text: The <disfmarker> the <disfmarker> the log of X plus N is not the same as the log of E to the X plus E to the N .
speaker: E
text: Yeah .
speaker: E
text: But this i
speaker: E
text: Well , I don't <disfmarker>
speaker: E
text: Well , uh ,
speaker: B
text: Maybe we can take it off - line ,
speaker: E
text: maybe <disfmarker>
speaker: B
text: but I <disfmarker> I don't know .
speaker: E
text: I <disfmarker> I can do this incorrectly .
speaker: E
text: Well , the expression that appear in the <disfmarker> in the paper , <nonvocalsound> is , uh <disfmarker>
speaker: D
text: The log <disfmarker>
speaker: D
text: the Taylor series expansion for log one plus N by X is <disfmarker>
speaker: E
text: is X <disfmarker>
speaker: C
text: Is it the first - order expansion ?
speaker: B
text: OK .
speaker: B
text: I i
speaker: D
text: Yeah , the first one .
speaker: C
text: Yeah , I guess .
speaker: D
text: Yeah .
speaker: D
text: Yeah .
speaker: B
text: OK .
speaker: C
text: Yeah .
speaker: B
text: Yeah . Cuz it doesn't just follow what 's there .
speaker: C
text: Uh - huh .
speaker: D
text: Y yeah . If <disfmarker> if you take log X into log one plus N by X , and then expand the log one plus N by X into Taylor series <disfmarker>
speaker: B
text: It has to be some , uh , Taylor series <disfmarker>
speaker: C
text: Yeah .
speaker: E
text: Now , this is the <disfmarker>
speaker: E
text: and then <disfmarker>
speaker: C
text: Yeah , but the <disfmarker> the second <pause> expression that you put is the first - order expansion of the nonlinear relation between <disfmarker>
speaker: E
text: Not exactly .
speaker: B
text: No .
speaker: E
text: No , no , no .
speaker: E
text: It 's not the first space .
speaker: E
text: Well , we have <disfmarker> pfft , uh , em <disfmarker>
speaker: E
text: Well , we can put that X is equal <disfmarker> I is equal to log of , uh ,
speaker: E
text: mmm <disfmarker>
speaker: B
text: That doesn't follow .
speaker: E
text: Well , we can put , uh , this ?
speaker: D
text: Mmm .
speaker: D
text: No .
speaker: B
text: That <disfmarker> I mean , that <disfmarker> the f top one does not <pause> imply the second one .
speaker: E
text: The top ?
speaker: B
text: Because <disfmarker> cuz the log of a sum is not the same as <pause> th
speaker: E
text: Yeah , yeah , yeah , yeah , yeah .
speaker: B
text: I mean , as <disfmarker>
speaker: B
text: Yeah .
speaker: E
text: But we can <disfmarker>
speaker: E
text: uh , we <disfmarker> we know that , for example , the log of <vocalsound> E plus B is equal to log of E plus log to B .
speaker: B
text: Right .
speaker: E
text: And we can say here , it i
speaker: B
text: Right . So you could s
speaker: C
text: What is that ?
speaker: E
text: And we can , uh , put this inside .
speaker: B
text: Yeah .
speaker: E
text: And then we can ,
speaker: E
text: uh ,
speaker: E
text: you know <disfmarker>
speaker: B
text: N no ,
speaker: B
text: but <disfmarker>
speaker: E
text: Yeah .
speaker: D
text: Uh .
speaker: B
text: I don't see how you get the second expression from the top one .
speaker: B
text: The <disfmarker> I mean , just more generally here , <vocalsound> if you say " log of , um , A plus B " ,
speaker: B
text: the log of <disfmarker> log of A plus B is not <disfmarker>
speaker: B
text: or A plus B is not the , um , log of E to the A plus E to the B .
speaker: E
text: No , no , no , no , no , no , no .
speaker: E
text: This not .
speaker: E
text: No .
speaker: B
text: Right ?
speaker: B
text: And that 's what you seem to be saying .
speaker: E
text: No .
speaker: E
text: It 's not . But this is the same <disfmarker>
speaker: E
text: oh .
speaker: B
text: Right ?
speaker: B
text: Cuz you <disfmarker> cuz you <disfmarker> up here you have the A plus B <disfmarker>
speaker: E
text: No .
speaker: E
text: I say if I apply log , I have , uh , log of E is equal to log of , uh <disfmarker> in this side , is equal to log of X
speaker: B
text: Plus N .
speaker: E
text: plus N .
speaker: B
text: Right .
speaker: E
text: No ?
speaker: E
text: Right .
speaker: B
text: Right .
speaker: B
text: And then how do you go from there to the <disfmarker> ?
speaker: E
text: This is right .
speaker: E
text: And then if I apply exponential , to have here E <disfmarker>
speaker: B
text: Look .
speaker: B
text: OK ,
speaker: B
text: so let 's <disfmarker>
speaker: B
text: I mean , C equals A plus B ,
speaker: C
text: It 's log o of capital Y .
speaker: C
text: Yeah , right .
speaker: B
text: and then <disfmarker>
speaker: E
text: Yeah .
speaker: C
text: Capital <pause> Y .
speaker: D
text: X . X .
speaker: D
text: This is X , inside .
speaker: C
text: Mm - hmm .
speaker: E
text: We have this ,
speaker: B
text: Right .
speaker: E
text: no ?
speaker: B
text: Yeah .
speaker: B
text: That one 's right .
speaker: E
text: Mm - hmm .
speaker: D
text: One and <disfmarker>
speaker: E
text: S uh , i th we can put here the set transformation .
speaker: B
text: Oh .
speaker: B
text: I see .
speaker: E
text: No ?
speaker: B
text: I see .
speaker: B
text: OK , I understand now .
speaker: B
text: Alright , thanks .
speaker: E
text: Yeah .
speaker: E
text: In this case , well , we can put here a <nonvocalsound> Y .
speaker: B
text: OK .
speaker: B
text: So , yeah .
speaker: B
text: It 's just by definition <pause> that the individual <disfmarker> <vocalsound> that the , uh <disfmarker>
speaker: B
text: So , capital X is by definition the same as E to the little X
speaker: B
text: because she 's saying that the little X is <disfmarker> is the , uh <disfmarker> is the log .
speaker: B
text: Alright .
speaker: E
text: Now we can put this .
speaker: B
text: Yeah .
speaker: E
text: No ?
speaker: E
text: And here we can multiply by X .
speaker: B
text: Alright .
speaker: B
text: I think these things are a lot clearer when you can use fonts <disfmarker> different fonts there
speaker: E
text: Oh , yes .
speaker: B
text: so you know which is which .
speaker: E
text: Yeah , yeah .
speaker: B
text: But I <disfmarker> I under I understand what you mean now .
speaker: E
text: That 's true .
speaker: E
text: That 's true .
speaker: B
text: OK .
speaker: E
text: But this <disfmarker> this is correct ?
speaker: B
text: Sure .
speaker: E
text: And now I can do it ,
speaker: E
text: uh <disfmarker>
speaker: E
text: pfff !
speaker: E
text: I can put log <nonvocalsound> of EX <vocalsound> plus log <disfmarker>
speaker: B
text: Oh .
speaker: B
text: Yes . I understand now .
speaker: E
text: And this is <disfmarker>
speaker: B
text: And that 's where it comes from .
speaker: B
text: Yeah .
speaker: C
text: Yeah . Right .
speaker: B
text: Right .
speaker: E
text: Now it 's correct .
speaker: B
text: Right .
speaker: B
text: OK .
speaker: B
text: Thanks .
speaker: E
text: Well . The idea <disfmarker>
speaker: E
text: Well ,
speaker: E
text: we have fixed this equa
speaker: B
text: OK . So now once you get that <disfmarker> that one , then you <disfmarker> then you do a first or second - order , or something , Taylor <vocalsound> series expansion of this .
speaker: E
text: Yeah .
speaker: E
text: This is another linear relation that this <disfmarker> to develop this in <vocalsound> vector s Taylor series .
speaker: C
text: Yeah , sure .
speaker: B
text: Right .
speaker: E
text: Mm - hmm .
speaker: E
text: And for that , well , the goal is to obtain , um <disfmarker> <vocalsound> <vocalsound> est estimate a PDF for the noisy speech
speaker: E
text: when we have a <disfmarker> <vocalsound> a statistic for clean speech and for the noisy speech .
speaker: E
text: Mmm ?
speaker: E
text: And when w
speaker: E
text: the way to obtain the PDF for the noisy speech is <disfmarker>
speaker: E
text: well , we know this statistic
speaker: E
text: and we know the noisy st
speaker: E
text: well , we can apply first order of the vector st Taylor series of the <disfmarker> of the <disfmarker> of <disfmarker> well , the order that we want , increase the complexity of the problem .
speaker: B
text: Mm - hmm .
speaker: E
text: And then when we have a expression , uh , for the <vocalsound> mean and variance of the noisy speech , we apply a technique of minimum mean - square estimation
speaker: B
text: Mm - hmm .
speaker: E
text: to obtain the expected value of the clean speech given the <disfmarker> this <vocalsound> statistic for the noisy speech <disfmarker>
speaker: B
text: Mm - hmm .
speaker: E
text: the statistic for clean speech and the statistic of the noisy speech .
speaker: E
text: This only that .
speaker: E
text: But the idea is that <disfmarker>
speaker: C
text: And the <disfmarker> the model of clean speech is a codebook . Right ?
speaker: E
text: u
speaker: E
text: Yeah .
speaker: E
text: We have our codebook with different density <vocalsound> Gaussian .
speaker: B
text: Mm - hmm .
speaker: E
text: We can expre we can put that the <vocalsound> PDF <comment> for the clean test , probability of the clean speech is equal to <disfmarker>
speaker: B
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: B
text: So , um ,
speaker: B
text: how <disfmarker> h
speaker: B
text: how much <disfmarker> in <disfmarker> in the work they reported , how much noisy speech did you need to get , uh , good enough statistics
speaker: B
text: for the <disfmarker> to get this mapping ?
speaker: E
text: I don't know exactly .
speaker: B
text: Yeah .
speaker: E
text: I <disfmarker> I need to s
speaker: B
text: Yeah .
speaker: E
text: I don't know exactly .
speaker: B
text: Cuz I think what 's certainly characteristic of a lot of the <pause> data in this test is that , um , you don't have <disfmarker> <vocalsound> the <disfmarker>
speaker: B
text: the training set may not be a <disfmarker> a great estimator for the noise in the test set .
speaker: B
text: Sometimes it is
speaker: B
text: and sometimes it 's not .
speaker: E
text: Yeah .
speaker: E
text: I <disfmarker> the clean speech <disfmarker> the codebook for clean speech , I am using TIMIT .
speaker: E
text: And I have now , uh , sixty - four <nonvocalsound> Gaus - Gaussian .
speaker: B
text: Uh - huh .
speaker: B
text: And what are you using for the noisy <disfmarker> ?
speaker: B
text: Y y doing that strictly <disfmarker>
speaker: E
text: Of the noise <disfmarker>
speaker: E
text: I estimate the noises wi
speaker: E
text: Well , for the noises I only use one Gaussian .
speaker: B
text: Mm - hmm .
speaker: B
text: And <disfmarker> and you <disfmarker> and you train it up entirely from , uh , nonspeech sections in the test ?
speaker: C
text: Hmm .
speaker: E
text: Uh , yes .
speaker: E
text: The first experiment that I do it is solely to calculate the , mmm <disfmarker> well , this value <disfmarker>
speaker: B
text: Yeah .
speaker: E
text: uh , the compensation of the dictionary o one time
speaker: E
text: using the <disfmarker> the noise at the f beginning of the sentence .
speaker: E
text: This is the first experiment .
speaker: B
text: Mm - hmm .
speaker: B
text: Yeah .
speaker: E
text: And I fix this for all the <disfmarker> all the sentences .
speaker: E
text: Uh , because <disfmarker>
speaker: E
text: well , the VTS methods <disfmarker>
speaker: E
text: In fact the first thing that I do is to <disfmarker> to obtain , uh , an expression for E <disfmarker>
speaker: E
text: probability e expression of <disfmarker> of E .
speaker: E
text: That mean that the VTS <disfmarker> mmm , with the VTS we obtain ,
speaker: E
text: uh <disfmarker>
speaker: E
text: well , we <disfmarker> we obtain the means for each Gaussian <comment> and the variance .
speaker: B
text: Mm - hmm .
speaker: E
text: This is one .
speaker: E
text: Eh , this is the composition of the dictionary .
speaker: B
text: Mm - hmm .
speaker: E
text: This one thing .
speaker: E
text: And the other thing that this <disfmarker> with these methods is to , uh , obtain <disfmarker> to calculate this value .
speaker: B
text: Mm - hmm .
speaker: E
text: Because we can write <disfmarker>
speaker: E
text: uh , we can write that <vocalsound> the estimation of the clean speech is equal at an expected value of the clean speech conditional to , uh , the noise signal <disfmarker> <vocalsound> the probability f of the <disfmarker> the statistic of the clean speech and the statistic of the noise .
speaker: B
text: Mm - hmm .
speaker: B
text: Mm - hmm .
speaker: E
text: This is the methods that say that we 're going obtain this .
speaker: B
text: Mm - hmm .
speaker: E
text: And we can put that this is equal to the estimated value of E minus a function that conditional to E to the T <disfmarker> to the noise signal .
speaker: E
text: Well , this is <disfmarker> this function is the <vocalsound> the term <disfmarker> after develop this , the term that we <disfmarker> we take .
speaker: E
text: Give PX and , uh , P the noise .
speaker: D
text: X K C noise .
speaker: B
text: Mmm .
speaker: E
text: And I can <vocalsound> put that this is equal to <pause> the <pause> noise signal minus <disfmarker>
speaker: E
text: Well , I put before <pause> this name ,
speaker: E
text: uh <disfmarker>
speaker: E
text: And I can calculate this .
speaker: B
text: What is the first variable in that probability ?
speaker: E
text: Uh , this is the Gaussian .
speaker: B
text: No , no . I 'm sorry .
speaker: B
text: In <disfmarker> in the one you pointed at .
speaker: E
text: v
speaker: B
text: What 's that variable ?
speaker: E
text: Uh , this is the <disfmarker>
speaker: D
text: Weak .
speaker: D
text: So probably it <disfmarker> it would do that .
speaker: E
text: like this ,
speaker: C
text: It 's one mixture of the model . Right ?
speaker: E
text: but conditional .
speaker: E
text: No , it 's condition
speaker: E
text: it 's not exactly this .
speaker: E
text: It 's modify .
speaker: E
text: Uh , if we have clean speech <disfmarker> we have the dictionary for the clean speech , we have a probability f of <disfmarker> our <disfmarker> our weight for each Gaussian .
speaker: E
text: No .
speaker: E
text: And now , this weight is different now
speaker: B
text: OK .
speaker: B
text: Yes .
speaker: E
text: because it 's conditional .
speaker: E
text: And this I need to <disfmarker> to calcu
speaker: E
text: I know this
speaker: B
text: Uh - huh .
speaker: E
text: and I know this
speaker: E
text: because this is from the dictionary that you have .
speaker: B
text: Uh - huh .
speaker: E
text: I need to calculate this .
speaker: E
text: And for calculate this , <vocalsound> I have an <disfmarker> I <disfmarker> I can develop an expression that is
speaker: B
text: Yes .
speaker: D
text: It 's overlapping .
speaker: E
text: that .
speaker: E
text: I can calculate <disfmarker> I can <disfmarker> I calculated this value , <vocalsound> uh , with the statistic of the noisy speech that I calculated before with the VTS approximation .
speaker: B
text: Mm - hmm .
speaker: E
text: And <disfmarker> well , normalizing .
speaker: E
text: And I know everything .
speaker: E
text: Uh , with the ,
speaker: E
text: nnn <disfmarker>
speaker: E
text: when I develop this in s Taylor <disfmarker> Taylor series , I can't , um , <vocalsound> calculate the mean and the variance <vocalsound> of the <disfmarker> for each of the Gaussian of the dictionary for the noisy speech .
speaker: E
text: Now .
speaker: E
text: And this is fixed .
speaker: B
text: Mm - hmm .
speaker: E
text: If I never do an estimat a newer estimation of the noise , this mean as <disfmarker> mean and the variance are fixed .
speaker: B
text: Mm - hmm .
speaker: E
text: And for each s uh , frame of the speech the only thing that I need to do is to calculate this
speaker: E
text: in order to calculate the estimation of the clean speech given our noisy speech .
speaker: B
text: So , I 'm <disfmarker> I 'm not following this perfectly
speaker: B
text: but , um ,
speaker: B
text: I <disfmarker>
speaker: B
text: Are you saying that all of these estimates are done <pause> using , um , estimates of the probability density for the noise that are calculated only from the first ten frames ?
speaker: E
text: Yeah .
speaker: B
text: And never change throughout anything else ?
speaker: E
text: Never cha
speaker: E
text: This is one of the approximations that I am doing .
speaker: B
text: Per <disfmarker> per <disfmarker> per utterance ,
speaker: B
text: or per <disfmarker> ?
speaker: E
text: Per utterance . Yes .
speaker: B
text: Per utterance .
speaker: E
text: Per utterance . Yes .
speaker: B
text: OK .
speaker: B
text: So it 's done <disfmarker> it 's done new for each new utterance .
speaker: E
text: And th
speaker: E
text: Yeah .
speaker: B
text: So this changes the whole mapping for every utterance .
speaker: E
text: It 's not <disfmarker>
speaker: E
text: Yeah .
speaker: E
text: Yeah .
speaker: E
text: It 's fixed , the dictionary .
speaker: B
text: OK .
speaker: E
text: And the other estimation is when I do the uh on - line estimation , I change the means and variance of th for the noisy speech
speaker: B
text: OK .
speaker: B
text: Yeah ?
speaker: E
text: each time that I detect noise .
speaker: B
text: Mm - hmm .
speaker: E
text: I do it uh again this
speaker: E
text: develop .
speaker: E
text: Estimate the new mean and the variance of the noisy speech .
speaker: E
text: And with th with this new s new mean and variance I estimate again this .
speaker: B
text: So you estimated , uh , f completely forgetting what you had before ?
speaker: E
text: Um ,
speaker: B
text: Uh , or is there some adaptation ?
speaker: E
text: no , no , no . It 's not completely <disfmarker>
speaker: E
text: No , it 's <disfmarker> I am doing something like an adaptation of the noise .
speaker: B
text: OK .
speaker: B
text: Now do we know , either from their experience or from yours , that , uh , just having , uh , two parameters , the <disfmarker> the mean and variance , is enough ?
speaker: B
text: Yeah .
speaker: B
text: I mean , I know you don't have a lot of data to estimate with ,
speaker: B
text: but <disfmarker> but , uh ,
speaker: B
text: um <disfmarker>
speaker: E
text: I estimate mean and variance for each one of the Gaussian of the codebook .
speaker: B
text: No , I 'm talking about the noise .
speaker: E
text: Oh ,
speaker: E
text: um . Well , only one <disfmarker>
speaker: B
text: There 's only one Gaussian .
speaker: E
text: I am only <disfmarker> using only one .
speaker: B
text: Right .
speaker: E
text: I don't know i
speaker: B
text: And you <disfmarker> and <disfmarker> and it 's ,
speaker: B
text: uh , uh <disfmarker>
speaker: B
text: right ,
speaker: B
text: it 's only <disfmarker>
speaker: B
text: it 's only one <disfmarker>
speaker: B
text: Wait a minute .
speaker: B
text: This is <disfmarker>
speaker: B
text: what 's the dimensionality of the Gaussian ?
speaker: B
text: This is <disfmarker>
speaker: E
text: Uh , it 's in <disfmarker> after the mel filter bank .
speaker: B
text: So this is twenty or something ?
speaker: E
text: Twenty - three .
speaker: B
text: Twenty ?
speaker: B
text: So it 's <disfmarker>
speaker: B
text: Yeah . So it 's actually forty numbers <pause> that you 're getting .
speaker: B
text: Yeah , maybe <disfmarker> maybe you don't have a <disfmarker>
speaker: E
text: Uh , the original paper say that only one Gaussian for the noise .
speaker: B
text: Well , yeah .
speaker: B
text: But , I mean , <vocalsound> no <disfmarker> no paper is <disfmarker> is a Bible ,
speaker: E
text: Yeah , maybe isn't the right thing .
speaker: B
text: you know .
speaker: B
text: This is <disfmarker> this is , uh <disfmarker>
speaker: E
text: Yeah , yeah , yeah .
speaker: B
text: The question is , um , <vocalsound> whether it would be helpful , i particularly if you used <disfmarker> if you had more <disfmarker>
speaker: B
text: So , suppose you did <disfmarker>
speaker: B
text: This is almost cheating .
speaker: B
text: It certainly isn't real - time .
speaker: B
text: But if y suppose you use the real boundaries that <disfmarker> that you were <disfmarker> in fact were given <vocalsound> by the VAD and so forth
speaker: B
text: or I <disfmarker> I guess we 're gonna be given even better boundaries than that .
speaker: B
text: And you look <disfmarker> you take all o all of the nonspeech components in an utterance ,
speaker: B
text: so you have a fair amount .
speaker: B
text: Do you benefit from having a better model for the noise ?
speaker: B
text: That would be another question .
speaker: E
text: Maybe .
speaker: B
text: So first question would be <vocalsound> to what extent i are the errors that you 're still seeing <vocalsound> based on the fact that you have poor boundaries for the , uh , uh , nonspeech ?
speaker: B
text: And the second question might be , given that you have good boundaries , could you do better if you used more parameters to characterize the noise ?
speaker: B
text: Um .
speaker: B
text: Also another question might be <disfmarker>
speaker: B
text: Um , they are doing <disfmarker> they 're using first term only of the vector Taylor series ?
speaker: E
text: Yeah .
speaker: B
text: Um , if you do a second term does it get too complicated cuz of the nonlinearity ?
speaker: E
text: Yeah . It 's quite complicated .
speaker: B
text: Yeah , OK .
speaker: B
text: No , I won't ask the next question then .
speaker: E
text: Oh , it 's <disfmarker> it 's the <disfmarker> for me it 's the first time that I am working with VTS .
speaker: E
text: Uh <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: No , it 's interesting .
speaker: B
text: Uh , w we haven't had anybody work with it before ,
speaker: B
text: so it 's interesting to get your <disfmarker> get your feedback about it .
speaker: E
text: It 's another type of approximation because i because it 's a statistic <disfmarker> statistic approximation to remove the noise .
speaker: E
text: I don't know .
speaker: B
text: Right .
speaker: F
text: Great .
speaker: F
text: OK .
speaker: F
text: Well , I guess we 're about done .
speaker: F
text: Um ,
speaker: F
text: so some of the digit forms don't have digits .
speaker: F
text: Uh , <vocalsound> we ran out
speaker: F
text: there were some blanks in there ,
speaker: F
text: so not everybody will be reading digits .
speaker: F
text: But , um ,
speaker: F
text: I guess you 've got some . Right , Morgan ?
speaker: B
text: I have some .
speaker: F
text: So ,
speaker: F
text: why don't you go ahead and start .
speaker: F
text: And I think it 's <pause> just us down here at this end that have them .
speaker: D
text: S
speaker: E
text: um
speaker: F
text: So .
speaker: B
text: Uh , OK .
speaker: D
text: S so , we switch off with this
speaker: F
text: Whenever you 're ready .
speaker: D
text: or n ?
speaker: F
text: Uh , leave it on ,
speaker: D
text: No . OK .
speaker: F
text: uh ,
speaker: B
text: They prefer to have them on
speaker: F
text: and the <disfmarker>
speaker: B
text: just so that they 're continuing to get the distant , uh , information .
speaker: F
text: Yeah .
speaker: D
text: OK .
speaker: D
text: OK .
speaker: F
text: OK .
speaker: B
text: OK . S

Meeting summary:
text: The ICSI Meeting Recorder Group at Berkeley met once more to discuss group members' progress.
text: The majority of the group are working on tasks related to the Aurora Project, including on-line normalization and Wiener filtering.
text: Other progress was also reported.
text: A large part of the meeting was spent discussing calculations and approaches using the white-board in the room.
text: At me013's behest, the group need to look closer at the errors made in tests on the aurora project, because the error rate may not be telling the whole picture.
text: Mn052 volunteers to run some experiments into how different numbers of MFCCs affect results.
text: Some previously reported results from me026 were determined to be garbage due to a bug in the code  Speaker mn052 also feels that his strange results are down to a bug.
text: This week, speaker mn007 has mostly been focusing on trying different approaches to on-line normalization, but making little impact on results.
text: He has also been playing with thresholding, effectively adding white noise to the data, but again with minimal affect.
text: Also making little improvement is fn002's work with Vectorial Taylor series, as a means of dealing with noise.
text: Mn052 has been adding Wiener filtering to the aurora task, and is thinking about future work on subspace.
text: Speaker me026 has been investigating phase normalization, and the possibility of adding spectral subtraction to an existing system.
text: Speaker me006 is still planning some cheating experiments to investigate features for recognition, alongside preparing for his quals.
text: also, the groups submission to the Eurospeech conference has been accepted.