Meeting transcript:
speaker: B
text: I think for two years we were two months , uh , away from being done .
speaker: A
text: And what was that , Morgan ?
speaker: A
text: What project ?
speaker: B
text: Uh , the , uh , TORRENT chip .
speaker: A
text: Oh .
speaker: B
text: Yeah .
speaker: B
text: We were two <disfmarker>
speaker: B
text: we were <disfmarker>
speaker: C
text: Yeah .
speaker: B
text: Uh , uh , we went through it <disfmarker> Jim and I went through old emails at one point
speaker: B
text: and <disfmarker> and for two years there was this thing saying , yeah , we 're <disfmarker> we 're two months away from being done .
speaker: B
text: It was very <disfmarker> very believable schedules , too .
speaker: B
text: I mean , we went through and <disfmarker> with the schedules <disfmarker> and we <disfmarker>
speaker: A
text: It was true for two years .
speaker: B
text: Yeah .
speaker: B
text: Oh , yeah .
speaker: B
text: It was very true .
speaker: A
text: So , should we just do the same kind of deal where we <pause> go around and do , uh , status report <pause> kind of things ?
speaker: A
text: OK .
speaker: A
text: And I guess when Sunil gets here he can do his last or something .
speaker: C
text: Mm - hmm .
speaker: A
text: So .
speaker: B
text: Yeah .
speaker: B
text: So we <pause> probably should wait for him to come before we do his .
speaker: A
text: OK .
speaker: F
text: OK .
speaker: A
text: That 's a good idea .
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: A
text: Any objection ?
speaker: A
text: Do y
speaker: B
text: All in favor
speaker: A
text: OK ,
speaker: A
text: M
speaker: A
text: Do you want to start , Morgan ?
speaker: A
text: Do you have anything ,
speaker: A
text: or <disfmarker> ?
speaker: B
text: Uh , I don't do anything .
speaker: B
text: I <disfmarker>
speaker: B
text: No , I mean , I <disfmarker> I 'm involved in discussions with <disfmarker> with people about what they 're doing ,
speaker: B
text: but I think they 're <disfmarker> since they 're here , they can talk about it themselves .
speaker: F
text: OK .
speaker: F
text: So should I go so that , uh ,
speaker: A
text: Yeah .
speaker: A
text: Why don't you go ahead , Barry ?
speaker: F
text: you 're gonna talk about Aurora stuff , per se ?
speaker: A
text: OK .
speaker: F
text: OK .
speaker: F
text: Um .
speaker: F
text: Well , this past week I 've just been , uh , getting down and dirty into writing my <disfmarker> my proposal .
speaker: F
text: So ,
speaker: F
text: um <disfmarker>
speaker: F
text: Mmm .
speaker: F
text: I just finished a section on , uh <disfmarker> on talking about these intermediate categories that I want to classify , um , as a <disfmarker> as a middle step .
speaker: F
text: And , um ,
speaker: F
text: I hope to <disfmarker> hope to get this , um <disfmarker> a full rough draft done by , uh , Monday so I can give it to Morgan .
speaker: A
text: When is your , uh , meeting ?
speaker: F
text: Um , my meeting
speaker: A
text: Yeah .
speaker: F
text: with , uh <disfmarker> ?
speaker: F
text: Oh , oh , you mean the <disfmarker> the quals .
speaker: A
text: The quals .
speaker: A
text: Yeah .
speaker: F
text: Uh , the quals are happening in July twenty - fifth .
speaker: A
text: Oh .
speaker: A
text: Soon .
speaker: F
text: Yeah .
speaker: F
text: D - Day .
speaker: A
text: Uh - huh .
speaker: A
text: Yeah .
speaker: F
text: Uh - huh .
speaker: A
text: So , is the idea you 're going to do this paper
speaker: A
text: and then you pass it out to everybody ahead of time
speaker: A
text: and <disfmarker> ?
speaker: F
text: Right , right .
speaker: F
text: So , y you write up a proposal , and give it to people ahead of time ,
speaker: F
text: and you have a short presentation .
speaker: F
text: And , um ,
speaker: F
text: and then , um <disfmarker> then everybody asks you questions .
speaker: A
text: Hmm .
speaker: F
text: Yeah .
speaker: A
text: I remember now .
speaker: F
text: Yep .
speaker: F
text: So , um .
speaker: A
text: Have you d ?
speaker: F
text: Y s
speaker: A
text: I was just gonna ask , do you want to say any <disfmarker> a little bit about it ,
speaker: A
text: or <disfmarker> ?
speaker: F
text: Oh .
speaker: F
text: Uh , a little bit about <disfmarker> ?
speaker: A
text: Mmm .
speaker: A
text: Wh - what you 're <disfmarker> what you 're gonna <disfmarker>
speaker: A
text: You said <disfmarker> you were talking about the , uh , particular features that you were looking at ,
speaker: F
text: Oh ,
speaker: F
text: the <disfmarker> the <disfmarker>
speaker: A
text: or <disfmarker>
speaker: F
text: Right .
speaker: F
text: Well , I was ,
speaker: F
text: um ,
speaker: F
text: I think one of the perplexing problems is ,
speaker: F
text: um ,
speaker: F
text: for a while I was thinking that I had to come up with a complete set of intermediate features <disfmarker> in intermediate categories to <disfmarker> to classify right away .
speaker: F
text: But what I 'm thinking now is , I would start with <disfmarker> with a reasonable set .
speaker: F
text: Something <disfmarker> something like , um , um <disfmarker> like , uh , re regular phonetic features ,
speaker: F
text: just to <disfmarker> just to start off that way .
speaker: F
text: And do some phone recognition .
speaker: F
text: Um , build a system that , uh , classifies these , um <disfmarker> these feat uh , these intermediate categories using , uh , multi - band techniques .
speaker: F
text: Combine them and do phon phoneme recognition .
speaker: F
text: Look at <disfmarker>
speaker: F
text: then I would look at the errors produced in the phoneme recognition and say , OK , well , I could probably reduce the errors if I included this extra feature or this extra intermediate category .
speaker: F
text: That would <disfmarker> that would reduce certain confusions over other confusions .
speaker: F
text: And then <disfmarker> and then <vocalsound> reiterate .
speaker: F
text: Um , build the intermediate classifiers .
speaker: F
text: Uh , do phoneme recognition .
speaker: F
text: Look at the errors .
speaker: F
text: And then postulate new <disfmarker> or remove , um , intermediate categories .
speaker: F
text: And then do it again .
speaker: A
text: So you 're gonna use TIMIT ?
speaker: F
text: Um , for that <disfmarker> for that part of the <disfmarker> the process , yeah , I would use TIMIT .
speaker: A
text: Mm - hmm .
speaker: F
text: And ,
speaker: F
text: um ,
speaker: F
text: then <disfmarker>
speaker: F
text: after <disfmarker> after , uh , um , doing TIMIT .
speaker: F
text: Right ?
speaker: F
text: Um , that 's <disfmarker> <vocalsound> that 's , um <disfmarker> that 's just the ph the phone recognition task .
speaker: A
text: Mm - hmm .
speaker: A
text: Yeah .
speaker: F
text: Uh , I wanted to take a look at , um , things that I could model within word .
speaker: F
text: So , I would mov I would then shift the focus to , um , something like Schw - Switchboard ,
speaker: F
text: uh , where I 'd <disfmarker> I would be able to , um <disfmarker> to model , um , intermediate categories that span across phonemes ,
speaker: F
text: not just within the phonemes , themselves ,
speaker: A
text: Mm - hmm .
speaker: F
text: um ,
speaker: F
text: and then do the same process there , um , on <disfmarker> on a large vocabulary task like Switchboard .
speaker: F
text: Uh ,
speaker: F
text: and for that <disfmarker>
speaker: F
text: for that part I would <disfmarker> I 'd use the SRI recognizer
speaker: F
text: since it 's already set up for <disfmarker> for Switchboard .
speaker: F
text: And I 'd run some <disfmarker> some sort of tandem - style processing with , uh , my intermediate classifiers .
speaker: A
text: Oh . So that 's why you were interested in getting your own features into the SRI files .
speaker: F
text: Yeah . That 's why I <disfmarker> I was asking about that .
speaker: A
text: Yeah .
speaker: F
text: Yeah .
speaker: A
text: Yeah .
speaker: F
text: Um ,
speaker: F
text: and
speaker: F
text: I guess that 's <disfmarker> that 's it .
speaker: F
text: Any <disfmarker> any questions ?
speaker: A
text: Sounds good .
speaker: A
text: So you just have a few more weeks ,
speaker: A
text: huh ?
speaker: F
text: Um , yeah .
speaker: F
text: A few more .
speaker: A
text: It 's about a month from now ?
speaker: F
text: It 's a <disfmarker> it 's a month and <disfmarker> and a week .
speaker: A
text: Yeah .
speaker: F
text: Yeah .
speaker: A
text: So , uh , you want to go next , Dave ?
speaker: A
text: And we 'll do <disfmarker>
speaker: E
text: Oh .
speaker: E
text: OK ,
speaker: E
text: sure .
speaker: E
text: So , um ,
speaker: E
text: last week I finally got results from the SRI system about this mean subtraction approach .
speaker: E
text: And , um , we <disfmarker> we got an improvement , uh , in word error rate , training on the TI - digits data set and testing on Meeting Recorder digits of , um , <vocalsound> six percent to four point five percent ,
speaker: E
text: um , on the n on the far - mike data
speaker: E
text: using PZM F ,
speaker: E
text: but , um , the near - mike performance worsened , um , from one point two percent to two point four percent .
speaker: E
text: And , um ,
speaker: E
text: wh why would that be , um , <vocalsound> considering that we actually got an improvement in near - mike performance using HTK ?
speaker: E
text: And so ,
speaker: E
text: uh , with some input from , uh , Andreas , I have a theory in two parts .
speaker: E
text: Um ,
speaker: E
text: first of all HTK <disfmarker> sorry , SR - the SRI system is doing channel adaptation ,
speaker: E
text: and so HTK wasn't .
speaker: E
text: Um ,
speaker: E
text: so this ,
speaker: E
text: um <disfmarker>
speaker: E
text: This mean subtraction approach will do a kind of channel <pause> normalization
speaker: E
text: and so that might have given the HTK use of it a boost that wouldn't have been applied in the SRI case .
speaker: E
text: And also , um , the <disfmarker> Andreas pointed out the SRI system is using more parameters .
speaker: E
text: It 's got finer - grained acoustic models .
speaker: E
text: So those finer - grained acoustic models could be more sensitive to the artifacts <pause> in the re - synthesized audio .
speaker: E
text: Um .
speaker: E
text: And me and Barry were listening to the re - synthesized audio
speaker: E
text: and sometimes it seems like you get of a bit of an echo of speech in the background .
speaker: E
text: And so that seems like it could be difficult for training ,
speaker: E
text: cuz you could have <pause> different phones <pause> lined up with a different foreground phone , <vocalsound> um , <vocalsound> depending on <pause> the timing of the echo .
speaker: E
text: So , um ,
speaker: E
text: I 'm gonna try training on a larger data set ,
speaker: E
text: and then , eh , the system will have seen more examples o of these artifacts and hopefully will be more robust to them .
speaker: E
text: So I 'm planning to use the Macrophone set of , um , read speech ,
speaker: E
text: and , um <disfmarker>
speaker: E
text: Hmm .
speaker: B
text: I had another thought just now ,
speaker: B
text: which is , uh , remember we were talking before about <disfmarker> we were talking in our meeting about , uh , this stuff that <disfmarker> some of the other stuff that Avendano did ,
speaker: B
text: where they were , um , getting rid of low - energy <pause> sections ?
speaker: B
text: Um ,
speaker: B
text: uh ,
speaker: B
text: if you <disfmarker>
speaker: B
text: if you did a high - pass filtering , as Hirsch did in <pause> late eighties to reduce some of the effects of reverberation , uh , uh , Avendano and Hermansky were arguing that , uh , perhaps one of the reasons for that working was ma may not have even been the filtering so much but the fact that when you filter a <disfmarker> an all - positive power spectrum you get some negative values ,
speaker: B
text: and you gotta figure out what to do with them if you 're gonna continue treating this as a power spectrum .
speaker: B
text: So , what <disfmarker> what Hirsch did was , uh , set them to zero <disfmarker>
speaker: B
text: set the negative values to zero .
speaker: B
text: So if you imagine a <disfmarker> a waveform that 's all positive ,
speaker: B
text: which is the time trajectory of energy ,
speaker: B
text: um , and , uh , shifting it downwards , and then getting rid of the negative parts , that 's essentially throwing away the low - energy things .
speaker: B
text: And it 's the low - energy parts of the speech where the reverberation is most audible .
speaker: B
text: You know , you have the reverberation from higher - energy things showing up in <disfmarker>
speaker: B
text: So in this case you have some artificially imposed <pause> reverberation - like thing .
speaker: B
text: I mean , you 're getting rid of some of the other effects of reverberation ,
speaker: B
text: but because you have these non - causal windows , you 're getting these funny things coming in ,
speaker: B
text: uh , at n
speaker: B
text: And ,
speaker: B
text: um ,
speaker: B
text: what if you did <disfmarker> ?
speaker: B
text: I mean , there 's nothing to say that the <disfmarker> the processing for this re - synthesis has to be restricted to trying to get it back to the original , according to some equation .
speaker: E
text: Uh - huh .
speaker: B
text: I mean , you also could , uh , just try to make it nicer .
speaker: E
text: Mm - hmm .
speaker: B
text: And one of the things you could do is , you could do some sort of VAD - like thing
speaker: B
text: and you actually could take very low - energy sections and set them to some <disfmarker> some , uh , very low or <disfmarker> or near zero <pause> value .
speaker: E
text: Uh - huh .
speaker: B
text: I mean ,
speaker: B
text: uh , I 'm just saying if in fact it turns out that <disfmarker> that these echoes that you 're hearing are , uh <disfmarker>
speaker: B
text: or pre - echoes ,
speaker: B
text: whichever they are <disfmarker>
speaker: B
text: are <disfmarker> are , uh , part of what 's causing the problem , you actually could get rid of them .
speaker: E
text: Uh - huh .
speaker: B
text: Be pretty simple .
speaker: E
text: OK .
speaker: B
text: I mean , you do it in a pretty conservative way
speaker: B
text: so that if you made a mistake you were more likely to <pause> keep in an echo than to throw out speech .
speaker: E
text: Hmm .
speaker: G
text: Um , what is the reverberation time <pause> like <pause> there ?
speaker: E
text: In thi in this room ?
speaker: G
text: On , uh , the <disfmarker> the one what <disfmarker> the s in the speech that you are <disfmarker> you are using like ?
speaker: E
text: Uh <disfmarker>
speaker: E
text: Y Yeah .
speaker: E
text: I <disfmarker> I <disfmarker> I <disfmarker> I don't know .
speaker: B
text: So , it 's this room .
speaker: G
text: It 's , uh <disfmarker>
speaker: G
text: Oh , this room ?
speaker: B
text: It 's <disfmarker> it 's this room .
speaker: G
text: OK .
speaker: B
text: So <disfmarker>
speaker: B
text: so it 's <disfmarker> these are just microphone <disfmarker>
speaker: B
text: this micro close microphone and a distant microphone , he 's doing these different tests on .
speaker: F
text: Oh .
speaker: B
text: Uh , we should do a measurement in here .
speaker: B
text: I g think we never have .
speaker: B
text: I think it 's <disfmarker> I would guess , uh , point seven , point eight seconds f uh , R T
speaker: F
text: Hmm !
speaker: B
text: something like that ?
speaker: G
text: Mm - hmm .
speaker: B
text: But it 's <disfmarker> you know , it 's this room .
speaker: B
text: So .
speaker: G
text: OK .
speaker: G
text: Mm - hmm .
speaker: B
text: Uh .
speaker: B
text: But the other thing is , he 's putting in <disfmarker> w
speaker: B
text: I was using the word " reverberation " in two ways .
speaker: B
text: He 's also putting in , uh , a <disfmarker>
speaker: B
text: he 's taking out some reverberation ,
speaker: B
text: but he 's putting in something ,
speaker: B
text: because he has <pause> averages over multiple windows stretching out to twelve seconds ,
speaker: B
text: which are then being subtracted from the speech .
speaker: B
text: And since , you know , what you subtract , sometimes you 'll be <disfmarker> you 'll be subtracting from some larger number
speaker: B
text: and sometimes you won't .
speaker: G
text: Mm - hmm . Mm - hmm .
speaker: B
text: And <disfmarker>
speaker: B
text: So you can end up with some components in it that are affected by things that are seconds away .
speaker: B
text: Uh , and if it 's a low <pause> energy compo portion , you might actually hear some <pause> funny things .
speaker: G
text: Yeah .
speaker: E
text: O o one thing , um , I noticed is that , um , the mean subtraction seems to make the PZM signals louder after they 've been re - synthesized .
speaker: E
text: So I was wondering , is it possible that one reason it helped with the Aurora baseline system is <pause> just as a kind of gain control ?
speaker: E
text: Cuz some of the PZM signals sound pretty quiet if you don't amplify them .
speaker: C
text: Mm - hmm .
speaker: C
text: I don't see why <disfmarker> why your signal is louder after processing ,
speaker: C
text: because yo
speaker: E
text: Yeah . I don't know why - y , uh , either .
speaker: C
text: Yeah .
speaker: B
text: I don't think just multiplying the signal by two would have any effect .
speaker: C
text: Mm - hmm .
speaker: E
text: Oh ,
speaker: E
text: OK .
speaker: B
text: Yeah .
speaker: B
text: I mean , I think if you really have louder signals , what you mean is that you have <pause> better signal - to - noise ratio .
speaker: C
text: Well , well <disfmarker>
speaker: B
text: So if what you 're doing is improving the signal - to - noise ratio , then it would be better .
speaker: C
text: Mm - hmm .
speaker: B
text: But just it being bigger if <disfmarker> with the same signal - to - noise ratio <disfmarker>
speaker: E
text: It w i i it wouldn't affect things .
speaker: C
text: Yeah .
speaker: E
text: OK .
speaker: B
text: No .
speaker: C
text: Well , the system is <disfmarker> use <pause> the absolute energy , so it 's a little bit dependent on <disfmarker> on the <pause> signal level .
speaker: C
text: But , not so much , I guess .
speaker: B
text: Well , yeah .
speaker: B
text: But it 's trained and tested on the same thing .
speaker: C
text: Mmm .
speaker: C
text: Mm - hmm .
speaker: B
text: So if the <disfmarker> if the <disfmarker> if you change <vocalsound> in both training and test , the absolute level by a factor of two , it will n have no effect .
speaker: C
text: Yeah .
speaker: A
text: Did you add <pause> this data to the training set , for the Aurora ?
speaker: A
text: Or you just tested on this ?
speaker: E
text: Uh <disfmarker>
speaker: E
text: Um .
speaker: E
text: Did I w what ?
speaker: E
text: Sorry ?
speaker: A
text: Well , Morgan was just saying that , uh , as long as you do it in both training and testing , it shouldn't have any effect .
speaker: E
text: Yeah .
speaker: A
text: But I <disfmarker> I was <pause> sort of under the impression that you just tested with this data .
speaker: E
text: I <disfmarker>
speaker: E
text: I b
speaker: A
text: You didn't <pause> train it also .
speaker: E
text: I <disfmarker>
speaker: E
text: Right .
speaker: E
text: I trained on clean TI - digits .
speaker: E
text: I <disfmarker> I did the mean subtraction on clean TI - digits .
speaker: E
text: But I didn't <disfmarker> I 'm not sure if it made the clean ti TI - digits any louder .
speaker: B
text: Oh , I see .
speaker: E
text: I only remember noticing it made the , um , PZM signal louder .
speaker: B
text: OK .
speaker: B
text: Well , I don't understand then .
speaker: B
text: Yeah .
speaker: E
text: Huh .
speaker: E
text: I don't know .
speaker: E
text: If it 's <disfmarker> if it 's <disfmarker> like , if it 's trying to find a <disfmarker> a reverberation filter , it could be that this reverberation filter is making things quieter .
speaker: E
text: And then if you take it out <disfmarker> that taking it out makes things louder .
speaker: E
text: I mean .
speaker: B
text: Uh , no .
speaker: B
text: I mean , <vocalsound> uh , there 's <disfmarker> there 's nothing inherent about removing <disfmarker> if you 're really removing ,
speaker: E
text: Nuh - huh .
speaker: E
text: The mean .
speaker: B
text: uh , r uh , then I don't <pause> see how that would make it louder .
speaker: E
text: OK .
speaker: E
text: Yeah ,
speaker: E
text: I see .
speaker: E
text: Yeah .
speaker: E
text: OK .
speaker: B
text: So it might be just some <disfmarker>
speaker: E
text: So I should maybe listen to that stuff again .
speaker: B
text: Yeah .
speaker: B
text: It might just be some artifact of the processing that <disfmarker> that , uh , if you 're <disfmarker>
speaker: B
text: Uh , yeah .
speaker: E
text: Oh . OK .
speaker: B
text: I don't know .
speaker: C
text: Eh
speaker: A
text: I wonder if there could be something like , uh <disfmarker> for s for the PZM data ,
speaker: A
text: uh , you know , if occasionally , uh , somebody hits the table or something , you could get a spike .
speaker: A
text: Uh .
speaker: A
text: I 'm just wondering if there 's something about the , um <disfmarker> you know , doing the mean normalization where , uh , it <disfmarker> it could cause <pause> you to have better signal - to - noise ratio .
speaker: A
text: Um .
speaker: B
text: Well , you know , there is this .
speaker: B
text: Wait a minute .
speaker: B
text: It <disfmarker> it <disfmarker> i maybe <disfmarker>
speaker: B
text: i
speaker: B
text: If , um <disfmarker>
speaker: B
text: Subtracting the <disfmarker> the mean log spectrum is <disfmarker> is <disfmarker> is like dividing by the spectrum .
speaker: B
text: So , depending what you divide by , if your <disfmarker> if s your estimate is off and sometimes you 're <disfmarker> you 're <disfmarker> you 're getting a small number , you could make it bigger .
speaker: A
text: Mm - hmm .
speaker: E
text: Mm - hmm .
speaker: B
text: So , it 's <disfmarker> it 's just a <disfmarker> a question of <disfmarker>
speaker: B
text: there 's <disfmarker> It <disfmarker> it could be that there 's some normalization that 's missing ,
speaker: B
text: or something
speaker: E
text: Mm - hmm .
speaker: B
text: to make it <disfmarker>
speaker: B
text: Uh , y you 'd think it shouldn't be larger ,
speaker: B
text: but maybe in practice it is .
speaker: E
text: Hmm .
speaker: B
text: That 's something to think about .
speaker: B
text: I don't know .
speaker: C
text: I had a question about the system <disfmarker> the SRI system .
speaker: C
text: So , <vocalsound> you trained it on TI - digits ?
speaker: C
text: But except this , it 's exactly the same system as the one that was tested before and that was trained on <pause> Macrophone .
speaker: C
text: Right ?
speaker: C
text: So on TI - digits it gives you one point two percent error rate
speaker: C
text: and on Macrophone it 's still O point eight .
speaker: C
text: Uh , but is it <pause> exactly the same system ?
speaker: E
text: Uh .
speaker: E
text: I think so .
speaker: E
text: If you 're talking about the Macrophone results that Andreas had about , um , a week and a half ago , I think it 's the same system .
speaker: C
text: Hmm .
speaker: C
text: Mm - hmm .
speaker: C
text: So you use VTL - uh , vocal tract length normalization and , um , like MLLR transformations also ,
speaker: E
text: Mm - hmm .
speaker: C
text: and <disfmarker>
speaker: E
text: That 's <disfmarker>
speaker: B
text: I 'm sorry ,
speaker: C
text: all that stuff .
speaker: B
text: was his point eight percent , er , a <disfmarker> a result on testing on Macrophone or <disfmarker> or training ?
speaker: C
text: It was <pause> training on Macrophone and testing <disfmarker> yeah , on <disfmarker> on meeting digits .
speaker: B
text: Oh .
speaker: B
text: So that was done already .
speaker: B
text: So we were <disfmarker>
speaker: B
text: Uh , and it 's point eight ?
speaker: B
text: OK .
speaker: C
text: Mm - hmm .
speaker: B
text: OK .
speaker: C
text: Yeah .
speaker: C
text: I <disfmarker> I 've just been text <comment> testing the new <pause> Aurora front - end with <disfmarker> well , Aurora system actually <disfmarker>
speaker: C
text: so front - end and HTK , um , acoustic models on the meeting digits
speaker: C
text: and it 's a little bit better than the previous system .
speaker: C
text: We have <disfmarker> I have two point seven percent error rate .
speaker: C
text: And before with the system that was proposed , it 's what ? It was three point nine .
speaker: C
text: So .
speaker: B
text: Oh , that 's a lot better .
speaker: C
text: We are getting better .
speaker: B
text: So , what <disfmarker> w ?
speaker: G
text: With the <disfmarker> with the HTK back - end ? What we have for Aurora ?
speaker: C
text: And <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: Two point seven .
speaker: G
text: I know in the meeting , like <disfmarker>
speaker: C
text: On the meeting we have two point seven .
speaker: G
text: Right .
speaker: G
text: Oh .
speaker: F
text: That 's with the new IIR filters ?
speaker: C
text: Uh . Yeah , yeah .
speaker: F
text: OK .
speaker: C
text: So , yeah ,
speaker: C
text: we have <pause> the new LDA filters ,
speaker: C
text: and <disfmarker>
speaker: C
text: I think , maybe <disfmarker> I didn't look , but one thing that makes a difference is this DC offset compensation .
speaker: C
text: Uh , eh <disfmarker> Do y did you have a look at <disfmarker> at the meet uh , meeting digits , if they have a DC component ,
speaker: C
text: or <disfmarker> ?
speaker: E
text: I <disfmarker>
speaker: E
text: I didn't .
speaker: E
text: No .
speaker: C
text: Oh .
speaker: B
text: Hmm .
speaker: G
text: No . The DC component could be negligible .
speaker: G
text: I mean , if you are <pause> recording it through a mike .
speaker: G
text: I mean , any <disfmarker> all of the mikes have the DC removal <disfmarker> some capacitor sitting right in <pause> that bias it .
speaker: B
text: Yeah .
speaker: B
text: But this <disfmarker>
speaker: B
text: uh , uh , uh , no .
speaker: B
text: Because , uh , there 's a sample and hold in the A - toD.
speaker: B
text: And these period these typically do have a DC offset .
speaker: G
text: Oh , OK .
speaker: B
text: And <disfmarker> and they can be surprisingly large .
speaker: B
text: It depends on the electronics .
speaker: G
text: Oh ,
speaker: G
text: so it is the digital <disfmarker>
speaker: G
text: OK .
speaker: G
text: It 's the A - toD that introduces the DC in .
speaker: B
text: Yeah . The microphone isn't gonna pass any DC .
speaker: G
text: Yeah . Yeah . Yeah .
speaker: B
text: But <disfmarker> but ,
speaker: G
text: OK .
speaker: B
text: typi
speaker: B
text: you know , unless <disfmarker>
speaker: B
text: Actually , there are <pause> instrumentation mikes that <disfmarker> that do pass <disfmarker> go down to DC .
speaker: B
text: But <disfmarker> but ,
speaker: G
text: Mm - hmm .
speaker: B
text: uh ,
speaker: B
text: no , it 's the electronics .
speaker: B
text: And they <disfmarker> and <disfmarker>
speaker: G
text: Mm - hmm .
speaker: B
text: then there 's amplification afterwards .
speaker: B
text: And you can get ,
speaker: B
text: I think it was <disfmarker>
speaker: B
text: I think it was in the <pause> Wall Street Journal data that <disfmarker> that <disfmarker>
speaker: B
text: I can't remember , one of the DARPA things .
speaker: B
text: There was this big DC - DC offset
speaker: A
text: Mm - hmm .
speaker: B
text: we didn't <disfmarker> we didn't know about for a while , while we were <pause> messing with it .
speaker: B
text: And we were getting these terrible results .
speaker: B
text: And then we were talking to somebody and they said , " Oh , yeah . Didn't you know ?
speaker: B
text: Everybody knows that .
speaker: B
text: There 's all this DC offset in th "
speaker: B
text: So , yes .
speaker: B
text: You can have DC offset in the data .
speaker: G
text: Oh , OK .
speaker: G
text: OK .
speaker: B
text: Yeah .
speaker: A
text: So was that <disfmarker> was that everything , Dave ?
speaker: E
text: Oh .
speaker: E
text: And I also , um , did some experiments <pause> about normalizing the phase .
speaker: E
text: Um .
speaker: E
text: So I c I came up with a web page that people can take a look at .
speaker: E
text: And , um ,
speaker: E
text: the interesting thing that I tried was , um , Adam and Morgan had this idea ,
speaker: E
text: um , since my original attempts to , um , take the mean of the phase spectra over time and normalize using that , by subtracting that off , didn't work . Um , so , well , that we thought that might be due to , um , problems with , um , the arithmetic of phases .
speaker: E
text: They <disfmarker> they add in this modulo two pi way
speaker: E
text: and ,
speaker: E
text: um ,
speaker: E
text: there 's reason to believe that that approach of taking the mean of the phase spectrum wasn't really <pause> mathematically correct .
speaker: E
text: So , <vocalsound> what I did instead is I <vocalsound> took the mean of the FFT spectrum without taking the log or anything , and then I took the phase of that ,
speaker: E
text: and I subtracted that phase <pause> off
speaker: E
text: to normalize .
speaker: E
text: But that , um , didn't work either .
speaker: B
text: See , we have a different interpretation of this .
speaker: B
text: He says it doesn't work .
speaker: B
text: I said , I think it works magnificently ,
speaker: B
text: but just not for the task we intended .
speaker: B
text: Uh , it gets rid of the speech .
speaker: F
text: Uh , gets rid of the speech .
speaker: A
text: What does it leave ?
speaker: B
text: Uh , it leaves <disfmarker> you know , it leaves the junk .
speaker: B
text: I mean , I <disfmarker> I think it 's <disfmarker> it 's tremendous .
speaker: F
text: Oh , wow .
speaker: B
text: You see , all he has to do is go back and reverse what he did before ,
speaker: B
text: and he 's really got something .
speaker: A
text: Well , could you take what was left over and then subtract that ?
speaker: B
text: Ex - exactly .
speaker: G
text: Yeah .
speaker: B
text: Yeah , you got it .
speaker: F
text: Yeah .
speaker: B
text: So , it 's <disfmarker> it 's a general rule .
speaker: G
text: Oh , it 's <disfmarker>
speaker: B
text: Just listen very carefully to what I say
speaker: B
text: and do the opposite .
speaker: B
text: Including what I just said .
speaker: E
text: And , yeah , that 's everything .
speaker: A
text: All set ?
speaker: A
text: Do you want to go , Stephane ?
speaker: C
text: Um . Yeah .
speaker: C
text: Maybe , concerning these d still , these meeting digits .
speaker: C
text: I 'm more interested in trying to figure out what 's still the difference between the SRI system and the Aurora system .
speaker: C
text: And <disfmarker>
speaker: C
text: Um .
speaker: C
text: Yeah . So , I think I will maybe train , like , gender - dependent models ,
speaker: C
text: because <pause> this is also one big difference between <pause> the two systems .
speaker: C
text: Um ,
speaker: C
text: the other differences were <pause> the fact that maybe the acoustic models of the SRI are more <disfmarker> SRI system are more complex .
speaker: C
text: But , uh , Chuck , you did some experiments with this
speaker: C
text: and
speaker: A
text: It didn't seem to help in the HTK system .
speaker: C
text: it was hard t to <disfmarker> to have some exper some improvement with this .
speaker: C
text: Um .
speaker: B
text: Well , it sounds like they also have <disfmarker>
speaker: B
text: he <disfmarker> he 's saying they have all these , uh , uh , different kinds of adaptation .
speaker: C
text: Mm - hmm .
speaker: B
text: You know , they have channel adaptation .
speaker: C
text: Yeah .
speaker: B
text: They have speaker adaptation .
speaker: C
text: Right .
speaker: C
text: Yeah .
speaker: F
text: Yeah .
speaker: B
text: Yeah . Yeah .
speaker: A
text: Well , there 's also the normalization .
speaker: A
text: Like they do ,
speaker: A
text: um <disfmarker>
speaker: A
text: I 'm not sure how they would do it when they 're working with the digits ,
speaker: C
text: The vocal tr
speaker: A
text: but , like , in the Switchboard data , there 's , um <disfmarker> conversation - side normalization for the <pause> non - C - zero
speaker: A
text: components ,
speaker: C
text: Yeah .
speaker: C
text: Yeah . This is another difference .
speaker: C
text: Their normalization works like on <disfmarker> on the utterance levels .
speaker: A
text: Mm - hmm .
speaker: C
text: But we have to do it <disfmarker>
speaker: C
text: We have a system that does it on - line .
speaker: C
text: So , it might be <disfmarker>
speaker: A
text: Right .
speaker: C
text: it might be better with <disfmarker>
speaker: C
text: it might be worse if the <pause> channel is constant ,
speaker: A
text: Yeah .
speaker: C
text: or <disfmarker>
speaker: C
text: Nnn .
speaker: G
text: And the acoustic models are like - k triphone models or <disfmarker> or is it the whole word ?
speaker: C
text: SRI <disfmarker> it 's <disfmarker> it 's tr
speaker: F
text: SRI .
speaker: G
text: Yeah .
speaker: C
text: Yeah .
speaker: C
text: I guess it 's triphones .
speaker: G
text: It 's triphone .
speaker: B
text: I think it 's probably more than that .
speaker: C
text: Huh .
speaker: B
text: I mean , so they <disfmarker> they have <disfmarker> I <disfmarker> I thin think they use these , uh , uh , genone things .
speaker: B
text: So there 's <disfmarker> there 's these kind of , uh , uh , pooled models
speaker: B
text: and <disfmarker> and they can go out to all sorts of dependencies .
speaker: G
text: Oh . It 's like the tied state .
speaker: B
text: So .
speaker: A
text: Mm - hmm .
speaker: B
text: They have tied states
speaker: B
text: and I think <disfmarker>
speaker: B
text: I <disfmarker> I <disfmarker> I don't real I 'm talk I 'm just guessing here .
speaker: G
text: OK .
speaker: B
text: But I think <disfmarker> I think they <disfmarker> they don't just have triphones .
speaker: B
text: I think they have a range of <disfmarker> of , uh , dependencies .
speaker: C
text: Mm - hmm .
speaker: G
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: F
text: Hmm .
speaker: C
text: And <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: Well .
speaker: C
text: Um .
speaker: C
text: Well , the first thing I <disfmarker> that I want to do is just maybe these gender things .
speaker: C
text: Uh .
speaker: C
text: And maybe see with <pause> Andreas if <disfmarker>
speaker: C
text: Well , I <disfmarker> I don't know <pause> how much it helps , what 's the model .
speaker: A
text: So <disfmarker>
speaker: A
text: so the n stuff on the numbers you got , the two point seven , is that using the same training data that the SRI system used and got one point two ?
speaker: C
text: That 's right .
speaker: C
text: So it 's the clean <pause> TI - digits training set .
speaker: A
text: So exact same training data ?
speaker: C
text: Right .
speaker: C
text: Mm - hmm .
speaker: A
text: OK .
speaker: C
text: I guess you used the clean training set .
speaker: E
text: Right .
speaker: E
text: For <disfmarker> with the SRI system <disfmarker>
speaker: C
text: Mm - hmm .
speaker: C
text: Well .
speaker: E
text: You know , the <disfmarker> the Aurora baseline is set up with these , um <disfmarker> <vocalsound> this version of the clean training set that 's been filtered with this G - seven - one - two filter ,
speaker: E
text: and ,
speaker: E
text: um , to train the SRI system on digits S - Andreas used the original TI - digits ,
speaker: E
text: um , under U doctor - speech data TI - digits ,
speaker: E
text: which don't have this filter .
speaker: E
text: But I don't think there 's any other difference .
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah .
speaker: B
text: So is that <disfmarker> ? Uh , are <disfmarker> are these results comparable ?
speaker: B
text: So you <disfmarker> you were getting with the , uh , Aurora baseline something like two point four percent <pause> on clean TI - digits , when , uh , training the SRI system with clean TR digits <disfmarker> <comment> TI - digits .
speaker: B
text: Right ?
speaker: E
text: Um . Uh - huh .
speaker: B
text: And <disfmarker>
speaker: B
text: Yeah .
speaker: B
text: And , so , is your two point seven comparable ,
speaker: B
text: where you 're , uh , uh , using , uh , the submitted system ?
speaker: C
text: Yeah .
speaker: C
text: I think so .
speaker: C
text: Yeah .
speaker: B
text: OK .
speaker: C
text: Mm - hmm .
speaker: B
text: So it 's <pause> about the same ,
speaker: E
text: W w it was one <disfmarker> one point two
speaker: B
text: maybe a little worse .
speaker: C
text: Ye
speaker: E
text: with the SRI system ,
speaker: E
text: I <disfmarker>
speaker: B
text: I 'm sorry .
speaker: C
text: Yeah .
speaker: C
text: The complete SRI system is one point two .
speaker: B
text: You <disfmarker> you were HTK .
speaker: C
text: Yeah .
speaker: B
text: Right ?
speaker: B
text: OK .
speaker: C
text: Mm - hmm .
speaker: B
text: That 's right .
speaker: B
text: So <disfmarker>
speaker: B
text: OK ,
speaker: B
text: so <pause> the comparable number then , uh <pause> for what you were talking about then , since it was HTK , would be the <pause> um , two point f
speaker: C
text: It was four point something .
speaker: C
text: Right ?
speaker: C
text: The HTK system with , uh , b
speaker: E
text: D d
speaker: B
text: Oh , right , right , right , right .
speaker: E
text: Do you mean the b ?
speaker: C
text: MFCC features <disfmarker>
speaker: E
text: The baseline Aurora - two system , trained on TI - digits , tested on Meeting Recorder near ,
speaker: E
text: I think we saw in it today ,
speaker: E
text: and it was about six point six percent .
speaker: C
text: Oh .
speaker: B
text: Right . Right , right , right .
speaker: B
text: OK .
speaker: B
text: Alright .
speaker: B
text: So <disfmarker>
speaker: C
text: So <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: The only difference is the features , right now ,
speaker: B
text: He 's doing some <pause> different things .
speaker: C
text: between this and <disfmarker>
speaker: B
text: Yes .
speaker: B
text: OK ,
speaker: B
text: good .
speaker: B
text: So they are helping .
speaker: C
text: Mm - hmm .
speaker: B
text: That 's good to hear .
speaker: C
text: They are helping .
speaker: B
text: Yeah .
speaker: C
text: Yeah .
speaker: C
text: Um .
speaker: C
text: Yeah .
speaker: C
text: And another thing I <disfmarker> I maybe would like to do is to <pause> just test the SRI system that 's trained on Macrophone <disfmarker>
speaker: C
text: test it on , uh , the noisy TI - digits ,
speaker: B
text: Yeah .
speaker: C
text: cuz I 'm still wondering <pause> where this <pause> improvement comes from .
speaker: C
text: When you train on Macrophone , it seems better on meeting digits .
speaker: C
text: But I wonder if it 's just because maybe <pause> Macrophone is acoustically closer to the meeting digits than <disfmarker> than TI - digit is ,
speaker: C
text: which is <disfmarker>
speaker: C
text: TI - digits are very <pause> clean recorded digits
speaker: B
text: Mm - hmm .
speaker: C
text: and <disfmarker>
speaker: C
text: Uh ,
speaker: A
text: You know , it would also be interesting to see , uh <disfmarker> to do the regular Aurora test ,
speaker: C
text: f s
speaker: A
text: um ,
speaker: A
text: but use the SRI system instead of HTK .
speaker: C
text: That 's <disfmarker>
speaker: C
text: Yeah . That 's what <pause> I wanted , just , uh <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: So , just using the SRI system , test it on <disfmarker> and test it on <pause> Aurora TI - digits .
speaker: C
text: Right .
speaker: A
text: Why not the full Aurora , uh , test ?
speaker: C
text: Um .
speaker: C
text: Yeah . There is this problem of multilinguality yet .
speaker: C
text: So we don't <disfmarker>
speaker: A
text: Mm - hmm .
speaker: B
text: You 'd have to train the SRI system with <disfmarker> with all the different languages .
speaker: C
text: i i
speaker: C
text: We would have to train on <disfmarker>
speaker: A
text: Right .
speaker: C
text: Yeah .
speaker: A
text: Yeah .
speaker: A
text: That 's what I mean .
speaker: A
text: So , like , comple
speaker: B
text: It 'd be a <pause> lot of work .
speaker: B
text: That 's the only thing .
speaker: C
text: Yeah .
speaker: C
text: It 's <disfmarker>
speaker: A
text: Mmm .
speaker: A
text: Well , I mean ,
speaker: C
text: Mmm .
speaker: A
text: uh ,
speaker: A
text: uh ,
speaker: A
text: I guess the work would be into getting the <disfmarker> the files in the right formats , or something .
speaker: A
text: Right ?
speaker: A
text: I mean <disfmarker>
speaker: C
text: Mm - hmm .
speaker: A
text: Because when you train up the Aurora system , you 're , uh <disfmarker> you 're also training on all the data .
speaker: C
text: That 's right .
speaker: C
text: Yeah .
speaker: A
text: I mean , it 's <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: I see .
speaker: C
text: Oh , so , OK .
speaker: C
text: Right .
speaker: C
text: I see what you mean .
speaker: B
text: That 's true ,
speaker: B
text: but I think that also when we 've had these meetings week after week , oftentimes people have not done the full arrange of things
speaker: B
text: because <disfmarker> on <disfmarker> on whatever it is they 're trying , because it 's a lot of work , even just with the HTK .
speaker: A
text: Mm - hmm .
speaker: A
text: Mm - hmm .
speaker: B
text: So , it 's <disfmarker> it 's a good idea ,
speaker: B
text: but it seems like <pause> it makes sense to do some pruning
speaker: A
text: Mm - hmm .
speaker: B
text: first with a <disfmarker> a test or two that makes sense for you ,
speaker: A
text: Yeah .
speaker: B
text: and then <pause> take the likely candidates and go further .
speaker: A
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah .
speaker: C
text: But , just testing on TI - digits would already give us some information <pause> about what 's going on .
speaker: C
text: And <disfmarker>
speaker: C
text: mm - hmm .
speaker: C
text: Uh , yeah .
speaker: C
text: OK .
speaker: C
text: Uh , the next thing is this <disfmarker> this VAD problem that ,
speaker: C
text: um ,
speaker: C
text: um <disfmarker>
speaker: C
text: So , I 'm just talking about the <disfmarker> the curves that I <disfmarker> I sent <disfmarker> <vocalsound> I sent you <disfmarker>
speaker: C
text: so , whi that shows that <vocalsound> when the SNR decrease , <vocalsound> uh , the current <pause> VAD approach doesn't drop much frames <pause> for some particular noises ,
speaker: C
text: uh , which might be then noises that are closer to speech , uh , acoustically .
speaker: B
text: I i Just to clarify something for me .
speaker: B
text: I
speaker: C
text: Mm - hmm .
speaker: B
text: They were supp Supposedly , in the next evaluation , they 're going to be supplying us with boundaries .
speaker: B
text: So does any of this matter ?
speaker: B
text: I mean , other than our interest in it .
speaker: C
text: Uh <disfmarker>
speaker: B
text: Uh <disfmarker>
speaker: C
text: Well .
speaker: C
text: First of all , the boundaries might be , uh <disfmarker> like we would have t two hundred milliseconds or <disfmarker> before and after speech .
speaker: C
text: Uh .
speaker: C
text: So removing more than that might still make <pause> a difference <pause> in the results .
speaker: C
text: And <disfmarker>
speaker: B
text: Do we <disfmarker> ? I mean , is there some reason that we think that 's the case ?
speaker: C
text: No .
speaker: C
text: Because we don't <disfmarker> didn't looked <pause> that much at that .
speaker: B
text: Yeah .
speaker: C
text: But , <vocalsound> still , I think it 's an interesting problem .
speaker: C
text: And <disfmarker>
speaker: B
text: Oh , yeah .
speaker: C
text: Um .
speaker: C
text: Yeah .
speaker: B
text: But maybe we 'll get some insight on that when <disfmarker> when , uh , the gang gets back from Crete .
speaker: B
text: Because <pause> there 's lots of interesting problems , of course .
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah ,
speaker: B
text: And then the thing is if <disfmarker> if they really are going to have some means of giving us <pause> fairly tight , uh , boundaries , then that won't be so much the issue .
speaker: C
text: yeah .
speaker: C
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: B
text: Um
speaker: B
text: But <vocalsound> I don't know .
speaker: G
text: Because w we were wondering whether that <pause> VAD is going to be , like , a realistic one or is it going to be some manual segmentation .
speaker: G
text: And then , like , if <disfmarker> if that VAD is going to be a realistic one , then we can actually use their markers to shift the point around , I mean , the way we want
speaker: G
text: to find a <disfmarker>
speaker: B
text: Mm - hmm .
speaker: G
text: I mean , rather than keeping the twenty frames , we can actually move the marker to a point which we find more <pause> suitable for us .
speaker: G
text: But if that is going to be something like a manual , uh , segmenter , then we can't <pause> use that information anymore ,
speaker: B
text: Right .
speaker: C
text: Mm - hmm .
speaker: G
text: because that 's not going to be the one that is used in the final evaluation .
speaker: B
text: Right .
speaker: G
text: So . We don't know what is the type of <pause> <vocalsound> <pause> VAD which they 're going to provide .
speaker: B
text: Yeah .
speaker: C
text: Yeah .
speaker: C
text: And actually there 's <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: There 's an <disfmarker> uh , I think it 's still for <disfmarker> even for the evaluation ,
speaker: C
text: uh , it might still be interesting to <vocalsound> work on this
speaker: C
text: because <pause> the boundaries apparently that they would provide is just , <vocalsound> um , starting of speech and end of speech <pause> uh , at the utterance level .
speaker: C
text: And <disfmarker>
speaker: C
text: Um .
speaker: G
text: With some <disfmarker> some gap .
speaker: C
text: So <disfmarker>
speaker: G
text: I mean , with some pauses in the center ,
speaker: G
text: provided they meet that <disfmarker> whatever the hang - over time which they are talking .
speaker: C
text: Yeah .
speaker: C
text: But when you have like , uh , five or six frames , both <disfmarker>
speaker: G
text: Yeah .
speaker: G
text: Then the they will just fill <disfmarker> fill it up .
speaker: C
text: it <disfmarker> it <disfmarker> with <disfmarker>
speaker: G
text: I mean , th <disfmarker>
speaker: G
text: Yeah .
speaker: C
text: Yeah .
speaker: B
text: So if you could get at some of that , uh <disfmarker>
speaker: C
text: So <disfmarker>
speaker: B
text: although that 'd be hard .
speaker: C
text: Yeah .
speaker: C
text: It might be useful for , like , noise estimation , and a lot of other <pause> things that we want to work on .
speaker: B
text: But <disfmarker> but <disfmarker>
speaker: G
text: Yeah .
speaker: B
text: Yeah .
speaker: B
text: Right .
speaker: B
text: OK .
speaker: C
text: But <disfmarker>
speaker: C
text: Mmm .
speaker: C
text: Yeah .
speaker: C
text: So I did <disfmarker> I just <pause> started to test <pause> putting together two VAD which was <disfmarker> was not much work actually .
speaker: C
text: Um ,
speaker: C
text: I im re - implemented a VAD that 's very close to the , <vocalsound> um , energy - based VAD <vocalsound> that , uh , the other Aurora guys use .
speaker: C
text: Um .
speaker: C
text: So , which is just putting a threshold on <pause> the noise energy ,
speaker: B
text: Mm - hmm .
speaker: C
text: and , detect detecting the first <pause> group of four frames <pause> that have a energy that 's above this threshold ,
speaker: C
text: and ,
speaker: C
text: uh ,
speaker: C
text: from this point , uh , tagging the frames there as speech .
speaker: C
text: So it removes <vocalsound> the first silent portion <disfmarker> portion of each utterance .
speaker: C
text: And it really removes it ,
speaker: C
text: um ,
speaker: C
text: still o on the noises where <pause> our MLP VAD doesn't <pause> work a lot .
speaker: B
text: Mmm .
speaker: C
text: Uh ,
speaker: C
text: and <disfmarker>
speaker: B
text: Cuz I would have thought that having some kind of spectral <pause> information ,
speaker: B
text: uh <disfmarker>
speaker: B
text: uh ,
speaker: B
text: you know , in the old days people would use energy
speaker: B
text: and zero crossings , for instance <disfmarker> uh , would give you some <pause> better performance .
speaker: B
text: Right ?
speaker: B
text: Cuz you might have low - energy fricatives or <disfmarker> or , uh <pause> stop consonants , or something like that .
speaker: C
text: Mm - hmm .
speaker: B
text: Uh .
speaker: C
text: Yeah .
speaker: C
text: So , your point is <disfmarker> will be to u use whatever <disfmarker>
speaker: B
text: Oh , that if you d if you use purely energy and don't look at anything spectral , then you don't have a good way of distinguishing between low - energy speech components and <pause> nonspeech .
speaker: C
text: Mm - hmm .
speaker: B
text: And , um ,
speaker: B
text: just as a gross generalization , most nonsp many nonspeech noises have a low - pass kind of characteristic ,
speaker: B
text: some sort of slope .
speaker: B
text: And <disfmarker> and most , um , low - energy speech components that are unvoiced have a <disfmarker> a high - pass kind of characteristic <disfmarker>
speaker: C
text: Mm - hmm .
speaker: B
text: an upward slope .
speaker: C
text: Yeah .
speaker: B
text: So having some kind of a <disfmarker>
speaker: B
text: uh , you know , at the beginning of a <disfmarker> of a <disfmarker> of an S sound for instance , just starting in , it might be pretty low - energy ,
speaker: C
text: Mm - hmm .
speaker: B
text: but it will tend to have this high - frequency component .
speaker: B
text: Whereas , <vocalsound> a <disfmarker> a lot of rumble , and background noises , and so forth will be predominantly low - frequency .
speaker: B
text: Uh , you know , by itself it 's not enough to tell you ,
speaker: B
text: but it plus energy is sort of <disfmarker>
speaker: C
text: Yeah .
speaker: B
text: it plus energy plus timing information is sort of <disfmarker>
speaker: C
text: Mm - hmm .
speaker: B
text: I mean , if you look up in Rabiner and Schafer from like twenty - five years ago or something , that 's sort of <pause> what they were using then .
speaker: C
text: Mm - hmm .
speaker: B
text: So it 's <disfmarker> it 's not a <disfmarker>
speaker: C
text: Mm - hmm .
speaker: F
text: Hmm .
speaker: C
text: So , yeah .
speaker: C
text: It <disfmarker> it might be that what I did is <disfmarker>
speaker: C
text: so , removes like <vocalsound> low , um , <vocalsound> uh <disfmarker> low - energy , uh , speech frames .
speaker: C
text: Because <pause> the way I do it is I just <disfmarker> I just combine the two decisions <disfmarker>
speaker: C
text: so , the one from the MLP and the one from the energy - based <disfmarker> with the <disfmarker> with the and <pause> operator .
speaker: C
text: So ,
speaker: C
text: I only <pause> keep the frames where the two agree <pause> that it 's speech .
speaker: C
text: So if the energy - based dropped <disfmarker> dropped low - energy speech , mmm , they <disfmarker> they are <disfmarker> they are lost .
speaker: C
text: Mmm .
speaker: B
text: Mm - hmm .
speaker: C
text: But s still , the way it 's done right now it <disfmarker> it helps on <disfmarker> on the noises where <disfmarker> it seems to help on the noises where <vocalsound> our VAD was not very <pause> good .
speaker: B
text: Well , I guess <disfmarker>
speaker: B
text: I mean ,
speaker: B
text: one could imagine combining them in different ways .
speaker: B
text: But <disfmarker> but ,
speaker: B
text: I guess what you 're saying is that the <disfmarker> the MLP - based one has the spectral information .
speaker: C
text: Yeah .
speaker: B
text: So .
speaker: C
text: But <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: But the way it 's combined wi is maybe done <disfmarker>
speaker: C
text: Well , yeah .
speaker: B
text: Well , you can imagine <disfmarker>
speaker: C
text: The way I use a an a " AND " operator is <disfmarker>
speaker: C
text: So , it <disfmarker> I , uh <disfmarker>
speaker: B
text: Is <disfmarker> ?
speaker: C
text: The frames that are dropped by the energy - based system are <disfmarker> are , uh , dropped , even if the , um , MLP decides to keep them .
speaker: B
text: Right .
speaker: B
text: Right .
speaker: B
text: And that might not be optimal ,
speaker: C
text: But , yeah .
speaker: B
text: but <disfmarker>
speaker: C
text: Mm - hmm .
speaker: B
text: but <disfmarker> I mean , I guess in principle what you 'd want to do is have a <disfmarker> <vocalsound> uh , a probability estimated by each one
speaker: A
text: No
speaker: B
text: and <disfmarker> and put them together .
speaker: C
text: Yeah .
speaker: C
text: Mmm . M Yeah .
speaker: A
text: Something that <disfmarker> that I 've used in the past is , um <disfmarker> when just looking at the energy , is to look at the derivative .
speaker: A
text: And you <pause> make your decision when the derivative is increasing for <pause> so many frames .
speaker: A
text: Then you say that 's beginning of speech .
speaker: C
text: Uh - huh .
speaker: A
text: But , I 'm <disfmarker> I 'm trying to remember if that requires that you keep some amount of speech in a buffer .
speaker: A
text: I guess it depends on how you do it .
speaker: A
text: But <pause> I mean , that 's <disfmarker> that 's been a useful thing .
speaker: B
text: Yeah .
speaker: C
text: Mm - hmm .
speaker: F
text: Mm - hmm .
speaker: G
text: Yeah . Well , every everywhere has a delay associated with it .
speaker: G
text: I mean , you still have to k always keep a buffer ,
speaker: A
text: Mm - hmm .
speaker: G
text: then only make a decision because <pause> you still need to smooth the <pause> decision further .
speaker: A
text: Right .
speaker: A
text: Right .
speaker: G
text: So that 's always there .
speaker: A
text: Yeah .
speaker: A
text: OK .
speaker: C
text: Well , actually if I don't <disfmarker> maybe don't want to work too much of <disfmarker> on it right now .
speaker: C
text: I just wanted to <disfmarker> to see if it 's <disfmarker> <vocalsound> what I observed was the re was caused by this <disfmarker> this VAD problem .
speaker: C
text: And it seems to be the case .
speaker: B
text: Mm - hmm .
speaker: C
text: Um .
speaker: C
text: Uh , the second thing is the <disfmarker> this spectral subtraction .
speaker: C
text: Um .
speaker: C
text: Um ,
speaker: C
text: which I 've just started yesterday to launch a bunch of , uh , <nonvocalsound> twenty - five experiments ,
speaker: C
text: uh , with different , uh , values for the parameters that are used .
speaker: C
text: So ,
speaker: C
text: it 's the Makhoul - type spectral subtraction which use <pause> an over - estimation factor .
speaker: C
text: So , we substr I subtract more , <vocalsound> <vocalsound> um , <nonvocalsound> <vocalsound> noise than the noise spectra that <pause> is estimated <pause> on the noise portion of the s uh , the utterances .
speaker: C
text: So I tried several , uh , over - estimation factors .
speaker: C
text: And after subtraction , I also add <pause> a constant noise ,
speaker: C
text: and I also try different , uh , <vocalsound> noise , uh , values
speaker: C
text: and we 'll see what happen .
speaker: B
text: Hmm .
speaker: C
text: Mm - hmm .
speaker: B
text: OK .
speaker: C
text: Mm - hmm .
speaker: C
text: But st still when we look at the ,
speaker: C
text: um <disfmarker>
speaker: C
text: Well , it depends on the parameters that you use ,
speaker: C
text: but for moderate over - estimation factors and moderate noise level that you add , you st have a lot of musical noise .
speaker: C
text: Um .
speaker: C
text: On the other hand , when you <pause> subtract more and when you add more noise , you get rid of this musical noise
speaker: C
text: but <pause> maybe you distort a lot of speech .
speaker: C
text: So .
speaker: C
text: Well .
speaker: C
text: Mmm .
speaker: C
text: Well , it <disfmarker> until now , it doesn't seem to help .
speaker: C
text: But
speaker: C
text: We 'll see .
speaker: C
text: So the next thing , maybe I <disfmarker> what I will <pause> try to <disfmarker> to do is just <pause> to try to smooth mmm , <vocalsound> the , um <disfmarker> to smooth the d the result of the subtraction ,
speaker: C
text: to get rid of the musical noise ,
speaker: C
text: using some kind of filter ,
speaker: C
text: or <disfmarker>
speaker: G
text: Can smooth the SNR estimate , also .
speaker: C
text: Yeah .
speaker: C
text: Right .
speaker: C
text: Mmm .
speaker: G
text: Your filter is a function of SNR . Hmm ?
speaker: C
text: Yeah .
speaker: C
text: So , to get something that 's <disfmarker> would be closer to <pause> what you tried to do with Wiener filtering .
speaker: G
text: Yeah .
speaker: C
text: And <disfmarker>
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah .
speaker: G
text: Actually , it 's ,
speaker: G
text: uh <disfmarker>
speaker: G
text: Uh .
speaker: G
text: I don't know ,
speaker: G
text: it 's <disfmarker>
speaker: G
text: go ahead .
speaker: G
text: And it 's <disfmarker>
speaker: C
text: It <disfmarker>
speaker: G
text: go ahead .
speaker: C
text: Maybe you can <disfmarker>
speaker: C
text: I think it 's <disfmarker>
speaker: C
text: That 's it for me .
speaker: G
text: OK .
speaker: G
text: So , uh <disfmarker>
speaker: G
text: u
speaker: G
text: th I 've been playing with this Wiener filter , like .
speaker: G
text: And there are <disfmarker> there were some bugs in the program ,
speaker: G
text: so I was p initially trying to clear them up .
speaker: G
text: Because one of the bug was <disfmarker> I was assuming that always the VAD <disfmarker> uh , the initial frames were silence .
speaker: G
text: It always started in the silence state ,
speaker: G
text: but it wasn't for some utterances .
speaker: G
text: So the <disfmarker> it wasn't estimating the noise initially ,
speaker: G
text: and then it never estimated ,
speaker: G
text: because I assumed that it was always silence .
speaker: C
text: Mm - hmm .
speaker: C
text: So this is on SpeechDat - Car Italian ?
speaker: G
text: Yeah .
speaker: G
text: SpeechDat - Car Italian .
speaker: C
text: So , in some cases s there are also <disfmarker>
speaker: G
text: Yeah .
speaker: G
text: There 're a few cases , actually , which I found later , that there are .
speaker: C
text: o
speaker: C
text: Uh - huh .
speaker: G
text: So that was one of the <pause> bugs that was there in estimating the noise .
speaker: G
text: And , uh , so once it was cleared , uh , I ran a few experiments with <pause> different ways of smoothing the estimated clean speech and how t estimated the noise and , eh , smoothing the SNR also .
speaker: G
text: And so the <disfmarker> the trend seems to be like , <vocalsound> uh , smoothing the <pause> current estimate of the clean speech for deriving the SNR ,
speaker: G
text: which is like <pause> deriving the Wiener filter ,
speaker: G
text: seems to be helping . Then updating it quite fast
speaker: G
text: using a very small time constant .
speaker: G
text: So we 'll have , like , a few results where the <disfmarker>
speaker: G
text: estimating the <disfmarker>
speaker: G
text: the <disfmarker> More smoothing is helping .
speaker: G
text: But still it 's like <disfmarker> it 's still comparable to the baseline .
speaker: G
text: I haven't got anything beyond the baseline .
speaker: G
text: But that 's , like , not using any Wiener filter .
speaker: G
text: And , uh , so I 'm <disfmarker> I 'm trying a few more experiments with different time constants for smoothing the noise spectrum , and smoothing the clean speech , and smoothing SNR .
speaker: G
text: So there are three time constants that I have .
speaker: G
text: So , I 'm just playing around .
speaker: G
text: So , one is fixed in the line , like <pause> Smoothing the clean speech is <disfmarker> is helping ,
speaker: G
text: so I 'm not going to change it that much .
speaker: G
text: But , the way I 'm estimating the noise and the way I 'm estimating the SNR , I 'm just trying <disfmarker> trying a little bit .
speaker: G
text: So , that h
speaker: G
text: And the other thing is , like , putting a floor on the , uh , SNR ,
speaker: G
text: because that <disfmarker> if
speaker: G
text: some <disfmarker> In some cases the clean speech is , like <disfmarker> when it 's estimated , it goes to very low values ,
speaker: G
text: so the SNR is , like , very low .
speaker: G
text: And
speaker: G
text: so that actually creates a lot of variance in the low - energy region of the speech .
speaker: G
text: So , I 'm thinking of , like , putting a floor also for the SNR so that it doesn't <pause> vary a lot in the low - energy regions .
speaker: G
text: And , uh . So . The results are , like <disfmarker>
speaker: G
text: So far I 've been testing only with the <pause> baseline , which is <disfmarker> which doesn't have any LDA filtering and on - line normalization .
speaker: G
text: I just want to separate the <disfmarker> the contributions out .
speaker: G
text: So it 's just VAD , plus the Wiener filter , plus the baseline system ,
speaker: G
text: which is , uh , just the spectral <disfmarker> I mean , the mel sp mel , uh , frequency coefficients .
speaker: G
text: Um .
speaker: G
text: And the other thing that I tried was <disfmarker> but I just <vocalsound> took of those , uh , <pause> <vocalsound> Carlos filters , which Hynek had ,
speaker: G
text: to see whether it really h helps or not .
speaker: G
text: I mean , it was just a <disfmarker> a run to see whether it really degrades or it helps .
speaker: G
text: And
speaker: G
text: it 's <disfmarker> it seems to be like it 's not <vocalsound> hurting a lot by just blindly picking up one filter
speaker: G
text: which is nothing but a <pause> four hertz <disfmarker> a band - pass m m filter on the cubic root of the power spectrum .
speaker: G
text: So , that was the filter that Hy - uh , Carlos had .
speaker: G
text: And
speaker: G
text: so <disfmarker>
speaker: G
text: Yeah . Just <disfmarker> just to see whether it really <disfmarker> it 's <disfmarker> it 's <disfmarker> is it worth trying or not .
speaker: G
text: So , it doesn't seems to be degrading a lot on that .
speaker: G
text: So there must be something that I can <disfmarker> that can be done with that type of noise compensation also ,
speaker: G
text: which <disfmarker> <vocalsound> I guess I would ask Carlos about that .
speaker: G
text: I mean , how <disfmarker> how he derived those filters
speaker: G
text: and <disfmarker>
speaker: G
text: and where d if he has any filters which are derived on OGI stories , added with some type of noise which <disfmarker> what we are using currently ,
speaker: G
text: or something like that .
speaker: G
text: So maybe I 'll <disfmarker>
speaker: B
text: This is cubic root of power spectra ?
speaker: G
text: Yeah .
speaker: G
text: Cubic root of power spectrum .
speaker: B
text: So , if you have this band - pass filter , you probably get n you get negative values .
speaker: B
text: Right ?
speaker: G
text: Yeah .
speaker: G
text: And I 'm , like , floating it to z zeros right now .
speaker: B
text: OK .
speaker: G
text: So it has , like <disfmarker> the spectrogram has , like <disfmarker>
speaker: G
text: Uh ,
speaker: G
text: it actually , uh , enhances the onset and offset of <disfmarker> I mean , the <disfmarker> the begin and the end of the speech .
speaker: G
text: So it 's <disfmarker> there seems to be , like , deep valleys in the begin and the end of , like , high - energy regions ,
speaker: G
text: because the filter has , like , a sort of Mexican - hat type structure .
speaker: B
text: Mm - hmm .
speaker: G
text: So , those are the regions where there are , like <disfmarker>
speaker: B
text: Mm - hmm .
speaker: G
text: when I look at the spectrogram , there are those deep valleys on the begin and the end of the speech .
speaker: G
text: But the rest of it seems to be , like , pretty nice .
speaker: B
text: Mm - hmm .
speaker: G
text: So .
speaker: G
text: That 's <pause> something I observe using that filter .
speaker: G
text: And <disfmarker>
speaker: G
text: Yeah .
speaker: G
text: There are a few <disfmarker>
speaker: G
text: very <disfmarker>
speaker: G
text: not a lot of <disfmarker>
speaker: G
text: because the filter doesn't have a <disfmarker> really a deep negative portion ,
speaker: G
text: so that it 's not really creating a lot of negative values in the cubic root .
speaker: G
text: So ,
speaker: G
text: I 'll <disfmarker> I 'll s may continue with that for some w
speaker: G
text: I 'll <disfmarker> I 'll <disfmarker> Maybe I 'll ask Carlos a little more about how to play with those filters ,
speaker: G
text: and <disfmarker> but while <pause> making this Wiener filter better .
speaker: G
text: So .
speaker: G
text: Yeah .
speaker: G
text: That <disfmarker> that 's it , Morgan .
speaker: B
text: Uh ,
speaker: B
text: last week you were also talking about building up the subspace <pause> stuff ?
speaker: G
text: Yeah .
speaker: G
text: I <disfmarker> I <disfmarker> I would actually m m didn't get enough time to work on the subspace last week .
speaker: G
text: It was mostly about <pause> finding those bugs
speaker: G
text: and
speaker: G
text: th you know , things ,
speaker: B
text: OK .
speaker: G
text: and I didn't work much on that .
speaker: A
text: How about you , Carmen ?
speaker: D
text: Well , I am still working with , eh , VTS .
speaker: D
text: And , one of the things that last week ,
speaker: D
text: eh , say here is that maybe the problem was with the diff
speaker: D
text: because the signal have different level of energy .
speaker: B
text: Hmm ?
speaker: D
text: And , maybe , talking with Stephane and with Sunil , we decide that maybe it was interesting to <disfmarker> to apply on - line normalization before applying VTS .
speaker: D
text: But then <vocalsound> we decided that that 's <disfmarker> it doesn't work absolutely , because we modified also the noise .
speaker: D
text: And <disfmarker>
speaker: D
text: Well , thinking about that , we <disfmarker> we then <disfmarker> we decide that maybe is a good idea .
speaker: D
text: We don't know .
speaker: D
text: I don't hav
speaker: D
text: I don't <disfmarker>
speaker: D
text: this is <disfmarker>
speaker: D
text: I didn't <pause> do the experiment yet <disfmarker> to apply VTS in cepstral domain .
speaker: B
text: The other thing <pause> is <disfmarker>
speaker: B
text: So <disfmarker> so , in <disfmarker> i i
speaker: B
text: and <disfmarker>
speaker: B
text: Not <disfmarker>
speaker: B
text: and C - zero would be a different <disfmarker>
speaker: B
text: So you could do a different normalization for C - zero than for other things anyway .
speaker: B
text: I mean , the other thing I was gonna suggest is that you could have <pause> two kinds of normalization with <disfmarker> with , uh , different time constants .
speaker: B
text: So ,
speaker: B
text: uh ,
speaker: B
text: you could do some normalization <vocalsound> s uh , before the VTS ,
speaker: B
text: and then do some other normalization after .
speaker: B
text: I don't know .
speaker: B
text: But <disfmarker> but C - zero certainly acts differently than the others do ,
speaker: D
text: Uh .
speaker: B
text: so that 's <disfmarker>
speaker: C
text: Mm - hmm .
speaker: D
text: Well , we s decide to m to <disfmarker> to obtain the new expression if we work in the cepstral domain .
speaker: D
text: And <disfmarker>
speaker: D
text: Well . I am working in that now ,
speaker: B
text: Uh - huh .
speaker: D
text: but <vocalsound> I 'm not sure if that will be usefu useful .
speaker: D
text: I don't know .
speaker: D
text: It 's k it 's k
speaker: D
text: It 's quite a lot <disfmarker> It 's a lot of work .
speaker: D
text: Well , it 's not too much ,
speaker: B
text: Uh - huh .
speaker: D
text: but this <disfmarker> it 's work .
speaker: D
text: And I want to know if <disfmarker> if we have some <pause> feeling that <pause> the result <disfmarker>
speaker: B
text: Yeah .
speaker: D
text: I <disfmarker> I would like to know if <disfmarker>
speaker: D
text: I don't have any feeling if this will work better than apply VTS aft in cepstral domain will work better than apply in m mel <disfmarker> in filter bank domain .
speaker: D
text: I r I 'm not sure .
speaker: D
text: I don't <disfmarker> I don't know absolutely nothing .
speaker: C
text: Mm - hmm .
speaker: B
text: Yeah . Well , you 're <disfmarker> I think you 're the first one here to work with VTS ,
speaker: B
text: so ,
speaker: B
text: uh , maybe we could call someone else up who has ,
speaker: B
text: ask them their opinion .
speaker: B
text: Uh ,
speaker: C
text: Mm - hmm .
speaker: B
text: I don't <disfmarker> I don't have a good feeling for it .
speaker: B
text: Um .
speaker: G
text: Pratibha .
speaker: C
text: Actually , the VTS that you tested before was in the log domain
speaker: C
text: and so <pause> the codebook is e e kind of dependent on the <pause> level of the speech signal .
speaker: D
text: Yeah ?
speaker: C
text: And <disfmarker>
speaker: C
text: So I expect it <disfmarker> If <disfmarker> if you have something that 's independent of this , I expect it to <disfmarker> it <disfmarker> to , uh , be a better model of speech .
speaker: D
text: To have better <disfmarker>
speaker: C
text: And .
speaker: C
text: Well .
speaker: B
text: You <disfmarker> you wouldn't even need to switch to cepstra .
speaker: B
text: Right ?
speaker: B
text: I mean , you can just sort of normalize the <disfmarker>
speaker: C
text: No .
speaker: C
text: We could normali norm I mean , remove the median .
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: D
text: Mm - hmm .
speaker: B
text: And then you have <pause> one number which is very dependent on the level cuz it is the level ,
speaker: B
text: and the other which isn't .
speaker: C
text: Mm - hmm .
speaker: C
text: Yeah .
speaker: C
text: But here also we would have to be careful about removing the mean <pause> of speech
speaker: C
text: and
speaker: D
text: Ye
speaker: C
text: not of noise .
speaker: C
text: Because it 's like <pause> first doing general normalization
speaker: D
text: Yea
speaker: C
text: and then noise removal ,
speaker: C
text: which is <disfmarker>
speaker: D
text: Yeah .
speaker: D
text: We <disfmarker>
speaker: D
text: I was thinking to <disfmarker> to <disfmarker> to estimate the noise <pause> with the first frames
speaker: D
text: and then apply the VAD ,
speaker: B
text: Mm - hmm .
speaker: C
text: Mm - hmm .
speaker: D
text: before the on - line normalization .
speaker: D
text: We <disfmarker> we see <disfmarker>
speaker: C
text: Mm - hmm .
speaker: D
text: Well ,
speaker: D
text: I am thinking <vocalsound> about that and working about that ,
speaker: B
text: Yeah .
speaker: D
text: but I don't have result this week .
speaker: B
text: Sure .
speaker: B
text: I mean , one of the things we 've talked about <disfmarker> maybe it might be star time to start thinking about pretty soon , is as we look at the pros and cons of these different methods , how do they fit in with one another ?
speaker: B
text: Because <pause> we 've talked about potentially doing some combination of a couple of them .
speaker: D
text: Mm - hmm .
speaker: B
text: Maybe <disfmarker> maybe pretty soon we 'll have some sense of what their <pause> characteristics are ,
speaker: B
text: so we can see what should be combined .
speaker: C
text: Mm - hmm .
speaker: A
text: Is that it ?
speaker: B
text: OK .
speaker: A
text: OK ?
speaker: B
text: Why don't we read some digits ?
speaker: A
text: Yep .
speaker: A
text: Want to go ahead , Morgan ?
speaker: B
text: Sure .
speaker: A
text: Transcript L dash two one five .
speaker: B
text: O K .

Meeting summary:
text: A typical progress report meeting for the ICSI Meeting Recorder Group at Berkeley.
text: Each of the group reported their most recent progress, and any results they have achieved.
text: This then prompted discussion about the reasons behind such findings, which were for the most part not as expected.
text: Topics the group touched upon included spectral subtraction, phase normalization, Voice activity detection, along with comparisons between systems.
text: *NA*
text: A couple of issues have arisen that need to be looked into further such as DC-offset and effects of PZM signals.
text: Speaker Fn002 is worried about running VTS in the cepstral domain, because it requires a lot of work, and it is not clear that it will be much better than running it in the Mel domain.
text: Similarly, since at the next stage of the project data will have marked boundaries, it is not clear that voice-activity detection is worth pursuing.
text: Speaker me026 has been trying mean subtraction on the SRI system, with good improvement for the far mike, though worse on near mike.
text: This contradicts previous findings for HTK, though he has some theories to explain the difference.
text: Has also been working on different phase normalization techniques, with no luck.
text: Speaker Mn007 has also been looking at differences, differences between the SRI system and the groups Aurora project, and again, there are a number of possible explanations.
text: He has also been looking at a problem with the VAD and SNR.
text: Mn052 has sorted bugs in implementation of wiener filtering, and has been investigating smoothing and also SNR.
text: Fn002 is ready to run experiment investigating VTS in the cepstral domain.
text: Speaker me006 is still working on his proposal.