Meeting transcript:
speaker: A
text: We 're going ?
speaker: A
text: OK .
speaker: B
text: OK .
speaker: A
text: Sh - Close your door on <disfmarker> door on the way out ?
speaker: B
text: Thanks .
speaker: A
text: Thanks .
speaker: B
text: Oh .
speaker: A
text: Yeah .
speaker: A
text: Probably wanna get this other door , too .
speaker: A
text: OK .
speaker: A
text: So .
speaker: A
text: Um . <vocalsound> <vocalsound> What are we talking about today ?
speaker: E
text: Uh , well , first there are perhaps these uh Meeting Recorder digits that we tested .
speaker: A
text: Oh , yeah . That was kind of uh interesting .
speaker: E
text: So .
speaker: A
text: The <disfmarker> both the uh <disfmarker> <vocalsound> the SRI System and the oth
speaker: E
text: Um .
speaker: A
text: And for one thing that <disfmarker> that sure shows the <vocalsound> difference between having a lot of uh training data <vocalsound> or not ,
speaker: E
text: Of data ?
speaker: E
text: Yeah .
speaker: A
text: uh , the uh <disfmarker> <vocalsound> The best kind of number we have on the English uh <disfmarker> on near microphone only is <disfmarker> is uh three or four percent .
speaker: E
text: Mm - hmm .
speaker: A
text: And uh it 's significantly better than that , using fairly simple front - ends <vocalsound> on <disfmarker> <vocalsound> on the uh <disfmarker> <vocalsound> uh , with the SRI system .
speaker: E
text: Mm - hmm .
speaker: A
text: So I th I think that the
speaker: A
text: uh <disfmarker>
speaker: A
text: But that 's <disfmarker> that 's using uh a <disfmarker> a pretty huge amount of data ,
speaker: A
text: mostly not digits , of course ,
speaker: A
text: but <disfmarker> but then again <disfmarker>
speaker: A
text: Well , yeah .
speaker: A
text: In fact , mostly not digits for the actual training the H M Ms whereas uh in this case we 're just using digits for training the H M
speaker: E
text: Yeah .
speaker: E
text: Right .
speaker: A
text: Did anybody mention about whether the <disfmarker> the SRI system is a <disfmarker> <vocalsound> is <disfmarker> is doing the digits um the wor as a word model or as uh a sub s sub - phone states ?
speaker: E
text: I guess it 's <disfmarker> it 's uh allophone models ,
speaker: A
text: Yeah .
speaker: E
text: so , well <disfmarker>
speaker: A
text: Probably .
speaker: A
text: Huh ?
speaker: E
text: Yeah . I think so ,
speaker: E
text: because it 's their very d huge , their huge system .
speaker: A
text: Yeah .
speaker: E
text: And .
speaker: E
text: But .
speaker: E
text: So . There is one difference <disfmarker>
speaker: E
text: Well , the SRI system <disfmarker> the result for the SRI system that are represented here are with adaptation .
speaker: E
text: So there is <disfmarker>
speaker: E
text: It 's their complete system and <disfmarker> including on - line uh unsupervised adaptation .
speaker: A
text: That 's true .
speaker: E
text: And if you don't use adaptation , the error rate is around fifty percent worse , I think , if I remember .
speaker: E
text: Yeah .
speaker: A
text: OK .
speaker: A
text: It 's tha it 's that much , huh ?
speaker: E
text: Nnn .
speaker: E
text: It 's <disfmarker>
speaker: E
text: Yeah .
speaker: E
text: It 's quite significant .
speaker: E
text: Yeah .
speaker: A
text: Oh .
speaker: A
text: OK .
speaker: A
text: Still .
speaker: E
text: Mm - hmm .
speaker: A
text: But <disfmarker> but uh what <disfmarker> what I think I 'd be interested to do given that , is that we <disfmarker> we should uh <vocalsound> take <disfmarker>
speaker: A
text: I guess that somebody 's gonna do this ,
speaker: A
text: right ? <disfmarker>
speaker: A
text: is to take some of these tandem things and feed it into the SRI system ,
speaker: A
text: right ?
speaker: E
text: Yeah .
speaker: A
text: Yeah .
speaker: E
text: We can do something like that .
speaker: E
text: Yeah .
speaker: A
text: Yeah . Because <disfmarker>
speaker: E
text: But <disfmarker>
speaker: E
text: But I guess the main point is the data because
speaker: E
text: uh <vocalsound> I am not sure .
speaker: E
text: Our back - end is <disfmarker> is fairly simple
speaker: E
text: but until now , well , the attempts to improve it or <disfmarker> have fail
speaker: E
text: Ah , well , I mean uh what Chuck tried to <disfmarker> to <disfmarker> to do
speaker: A
text: Yeah ,
speaker: A
text: but he 's doing it with the same data , right ?
speaker: E
text: Yeah .
speaker: A
text: I mean so to <disfmarker> <vocalsound> So there 's <disfmarker> there 's <disfmarker> there 's two things being affected .
speaker: E
text: So it 's <disfmarker>
speaker: E
text: Yeah .
speaker: A
text: I mean . One is that <disfmarker> that , you know , there 's something simple that 's wrong with the back - end .
speaker: A
text: We 've been playing a number of states
speaker: E
text: Mm - hmm .
speaker: A
text: uh I <disfmarker> I don't know if he got to the point of playing with the uh number of Gaussians yet
speaker: E
text: Mm - hmm .
speaker: A
text: but <disfmarker> but uh ,
speaker: A
text: uh , you know .
speaker: A
text: But , yeah , so far he hadn't gotten any big improvement ,
speaker: E
text: Mm - hmm .
speaker: A
text: but that 's all with the same amount of data which is pretty small .
speaker: E
text: Yeah .
speaker: E
text: Mmm .
speaker: A
text: And um .
speaker: E
text: So , yeah , we could retrain some of these tandem on <disfmarker> on huge <disfmarker>
speaker: A
text: Well , you could do that , but I 'm saying even with it not <disfmarker> with that part not retrained ,
speaker: E
text: Ah , yeah .
speaker: A
text: just <disfmarker> just using <disfmarker> having the H M Ms <disfmarker> much better H M
speaker: E
text: Just <disfmarker>
speaker: E
text: f for the HMM models .
speaker: E
text: Yeah .
speaker: A
text: Yeah .
speaker: E
text: Mm - hmm .
speaker: E
text: Mm - hmm .
speaker: A
text: Um . <vocalsound> But just train those H M Ms using different features ,
speaker: A
text: the features coming from our Aurora stuff .
speaker: E
text: Yeah .
speaker: A
text: So .
speaker: E
text: Yeah .
speaker: E
text: But <vocalsound> what would be interesting to see also is what <disfmarker> what <disfmarker>
speaker: E
text: perhaps it 's not related , the amount of data but the um recording conditions .
speaker: E
text: I don't know .
speaker: E
text: Because <vocalsound> it 's probably not a problem of noise , because our features are supposed to be robust to noise .
speaker: E
text: It 's not a problem of channel , because there is um <vocalsound> <vocalsound> normalization with respect to the channel .
speaker: A
text: Well , yeah .
speaker: E
text: So <disfmarker>
speaker: A
text: I <disfmarker> I <disfmarker> I 'm sorry .
speaker: A
text: What <disfmarker> what is the problem that you 're trying to explain ?
speaker: E
text: The <disfmarker> the fact that <disfmarker> the result with the tandem and Aurora system are <vocalsound> uh so much worse .
speaker: A
text: That the <disfmarker>
speaker: A
text: Oh .
speaker: A
text: So much worse ?
speaker: E
text: Yeah .
speaker: A
text: Oh .
speaker: A
text: I uh but I 'm <disfmarker> I 'm almost certain that it <disfmarker> it <disfmarker> <vocalsound> I mean , that it has to do with the um amount of training data .
speaker: E
text: It <disfmarker>
speaker: A
text: It <disfmarker> it 's <disfmarker> it 's orders of magnitude off .
speaker: E
text: Yeah but <disfmarker>
speaker: E
text: Yeah .
speaker: E
text: Yeah but we train only on digits and it 's <disfmarker> it 's a digit task ,
speaker: E
text: so .
speaker: E
text: Well .
speaker: A
text: But <disfmarker> but having a huge <disfmarker>
speaker: E
text: It <disfmarker>
speaker: A
text: If <disfmarker> <vocalsound> if you look at what commercial places do , they use a huge amount of data .
speaker: E
text: Mm - hmm .
speaker: E
text: Alright .
speaker: A
text: This is a modest amount of data .
speaker: E
text: Yeah .
speaker: E
text: Mm - hmm .
speaker: A
text: So . <vocalsound> I mean , ordinarily you would say " well , given that you have enough occurrences of the digits , you can just train with digits rather than with , you know " <disfmarker>
speaker: E
text: Mm - hmm .
speaker: A
text: But the thing is , if you have a huge <disfmarker>
speaker: A
text: in other words , do word models <disfmarker>
speaker: A
text: But if you have a huge amount of data then you 're going to have many occurrences of similar uh allophones .
speaker: E
text: Right .
speaker: E
text: Mmm .
speaker: E
text: Yeah .
speaker: A
text: And that 's just a huge amount of training for it .
speaker: A
text: So it 's <vocalsound> um <disfmarker> <vocalsound> I <disfmarker> I think it has to be that , because , as you say , this is , you know , this is near - microphone ,
speaker: E
text: Mm - hmm .
speaker: A
text: it 's really pretty clean data .
speaker: E
text: Mm - hmm .
speaker: A
text: Um .
speaker: A
text: Now , some of it could be the fact that
speaker: A
text: uh <disfmarker>
speaker: A
text: let 's see , in the <disfmarker> in these multi - train things did we include noisy data in the training ?
speaker: E
text: Yeah .
speaker: A
text: I mean , that could be hurting us actually , for the clean case .
speaker: E
text: Yeah .
speaker: E
text: Well , actually we see that the clean train for the Aurora proposals are <disfmarker> are better than the multi - train ,
speaker: A
text: It is if <disfmarker>
speaker: E
text: yeah .
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: A
text: Cuz this is clean data ,
speaker: A
text: and so that 's not too surprising .
speaker: E
text: Mm - hmm .
speaker: A
text: But um . Uh .
speaker: A
text: So .
speaker: E
text: Well , o I guess what I meant is that
speaker: E
text: well , let 's say if we <disfmarker> if we add enough data to train on the um on the Meeting Recorder digits , I guess we could have better results than this .
speaker: A
text: Uh - huh .
speaker: A
text: Mm - hmm .
speaker: E
text: And .
speaker: E
text: What I meant is that perhaps we can learn something uh from this ,
speaker: E
text: what 's <disfmarker> what 's wrong uh what <disfmarker> what is different between TI - digits and these digits
speaker: E
text: and <disfmarker>
speaker: A
text: What kind of numbers are we getting on TI - digits ?
speaker: E
text: It 's point eight percent ,
speaker: E
text: so .
speaker: A
text: Oh .
speaker: A
text: I see .
speaker: E
text: Four - Fourier .
speaker: A
text: So in the actual TI - digits database we 're getting point eight percent ,
speaker: E
text: Yeah .
speaker: E
text: Yeah .
speaker: A
text: and here we 're getting three or four <disfmarker> three ,
speaker: E
text: Mm - hmm .
speaker: A
text: let 's see , three for this ?
speaker: A
text: Yeah .
speaker: A
text: Sure , but I mean ,
speaker: A
text: um point eight percent is something like double uh or triple what people have gotten who 've worked very hard at doing that .
speaker: E
text: Mm - hmm .
speaker: A
text: And <disfmarker> and also , as you point out , there 's adaptation in these numbers also .
speaker: E
text: Mmm .
speaker: A
text: So if you , you know , put the ad adap take the adaptation off , then it <disfmarker> for the English - Near you get something like two percent .
speaker: A
text: And here you had , you know , something like three point four .
speaker: E
text: Mm - hmm .
speaker: A
text: And I could easily see that difference coming from this huge amount of data that it was trained on .
speaker: E
text: Mm - hmm .
speaker: A
text: So it 's <disfmarker>
speaker: A
text: You know , I don't think there 's anything magical here .
speaker: E
text: Yeah .
speaker: A
text: It 's , you know , we used a simple HTK system with a modest amount of data .
speaker: A
text: And this is a <disfmarker> a , you know , modern <vocalsound> uh system
speaker: E
text: Yeah .
speaker: A
text: uh has <disfmarker> has a lot of nice points to it .
speaker: E
text: Mm - hmm .
speaker: A
text: Um .
speaker: A
text: So . I mean , the HTK is an older HTK , even .
speaker: A
text: So .
speaker: E
text: Mm - hmm .
speaker: A
text: Yeah it <disfmarker> it 's not that surprising .
speaker: A
text: But to me it just <disfmarker> it just meant a practical <vocalsound> point that um if we want to <vocalsound> publish results on digits that <disfmarker> that people pay <vocalsound> attention to we probably should uh <disfmarker>
speaker: A
text: Cuz we 've had the problem before that you get <disfmarker> show some <vocalsound> nice improvement on something that 's <disfmarker> that 's uh , uh <disfmarker> it seems like too large a number ,
speaker: E
text: Mm - hmm .
speaker: A
text: and uh <vocalsound> uh people don't necessarily take it so seriously .
speaker: A
text: Um .
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: A
text: So the three point four percent for this uh is <disfmarker> is
speaker: A
text: uh <disfmarker>
speaker: A
text: So why is it <disfmarker>
speaker: A
text: It 's an interesting question though , still .
speaker: A
text: Why is <disfmarker> why is it three point four percent for the d the digits recorded in this environment as opposed to <vocalsound> the uh point eight percent for <disfmarker> for <disfmarker> for the original TI - digits database ?
speaker: A
text: Um .
speaker: E
text: Yeah .
speaker: E
text: th that 's <disfmarker> th that 's my point
speaker: A
text: Given <disfmarker> given the same <disfmarker>
speaker: E
text: I <disfmarker> I <disfmarker> I don't I <disfmarker>
speaker: A
text: Yeah . So ignore <disfmarker> ignoring the <disfmarker> the <disfmarker> the SRI system for a moment ,
speaker: E
text: Mm - hmm .
speaker: A
text: just looking at <vocalsound> the TI - di the uh tandem system ,
speaker: A
text: if we 're getting point eight percent , which , yes , it 's high .
speaker: A
text: It 's , you know , it <disfmarker> it 's not awfully high ,
speaker: E
text: Mm - hmm .
speaker: A
text: but it 's , you know <disfmarker> it 's <disfmarker> it 's high .
speaker: A
text: Um . <vocalsound> Why is it <vocalsound> uh four times as high , or more ?
speaker: E
text: Yeah , I guess .
speaker: A
text: Right ?
speaker: A
text: I mean , there 's <disfmarker> <vocalsound> even though it 's close - miked there 's still <disfmarker> there really is background noise .
speaker: E
text: Mm - hmm .
speaker: A
text: Um . And <vocalsound> uh I suspect when the TI - digits were recorded if somebody fumbled or said something wrong or something that they probably made them take it over .
speaker: E
text: Mm - hmm .
speaker: A
text: It was not <disfmarker> I mean there was no attempt to have it be realistic in any <disfmarker> in any sense at all .
speaker: E
text: Well .
speaker: E
text: Yeah .
speaker: E
text: And acoustically , it 's q it 's <disfmarker>
speaker: E
text: I listened .
speaker: E
text: It 's quite different .
speaker: E
text: TI - digit is <disfmarker> it 's very , very clean and it 's like studio recording
speaker: A
text: Mm - hmm .
speaker: E
text: whereas these Meeting Recorder digits sometimes you have breath noise
speaker: E
text: and
speaker: E
text: Mmm .
speaker: A
text: Right .
speaker: A
text: Yeah .
speaker: A
text: So I think they were <disfmarker>
speaker: E
text: It 's <nonvocalsound> not controlled at all , I mean .
speaker: A
text: Bless you .
speaker: B
text: Thanks .
speaker: A
text: I <disfmarker> Yeah . I think it 's <disfmarker> it 's <disfmarker>
speaker: E
text: Mm - hmm .
speaker: A
text: So . Yes .
speaker: E
text: But
speaker: A
text: It 's <disfmarker> I think it 's <disfmarker> it 's the indication it 's harder .
speaker: E
text: Yeah .
speaker: A
text: Uh . <vocalsound> Yeah and again , you know , i that 's true either way .
speaker: A
text: I mean so take a look at the uh <disfmarker> <vocalsound> um , the SRI results .
speaker: A
text: I mean , they 're much much better ,
speaker: A
text: but still you 're getting something like one point three percent for uh things that are same data as in T <disfmarker> TI - digits the same <disfmarker> same text .
speaker: E
text: Mm - hmm .
speaker: A
text: Uh . And uh , I 'm sure the same <disfmarker> same system would <disfmarker> would get , you know , point <disfmarker> point three or point four or something <vocalsound> on the actual TI - digits .
speaker: A
text: So this <disfmarker> I think , on both systems the <vocalsound> these digits are showing up as harder .
speaker: E
text: Mmm .
speaker: A
text: Um .
speaker: E
text: Mm - hmm .
speaker: A
text: Which I find sort of interesting cause I think this is closer to <disfmarker>
speaker: A
text: uh I mean it 's still read .
speaker: A
text: But I still think it 's much closer to <disfmarker> to what <disfmarker> what people actually face , <vocalsound> um when they 're <disfmarker> they 're dealing with people saying digits over the telephone .
speaker: A
text: I mean . <vocalsound> I don't think uh <disfmarker>
speaker: A
text: I mean , I 'm sure they wouldn't release the numbers ,
speaker: A
text: but I don't think that uh <vocalsound> the uh <disfmarker> the <disfmarker> the companies that <disfmarker> that do telephone <vocalsound> speech get anything like point four percent on their <vocalsound> digits .
speaker: A
text: I 'm <disfmarker> I 'm <disfmarker> I 'm sure they get <disfmarker>
speaker: A
text: Uh , I mean , for one thing people do phone up who don't have uh uh Middle America accents and
speaker: E
text: Mm - hmm .
speaker: A
text: it 's a we we it 's <disfmarker> it 's <disfmarker> it 's US .
speaker: A
text: it has <disfmarker> has many people <vocalsound> <vocalsound> who sound in many different ways .
speaker: A
text: So .
speaker: A
text: Um .
speaker: A
text: I mean .
speaker: A
text: OK .
speaker: A
text: That was that topic .
speaker: A
text: What else we got ?
speaker: E
text: Um .
speaker: A
text: Did we end up giving up on <disfmarker> on , any Eurospeech submissions ,
speaker: E
text: But <disfmarker>
speaker: A
text: or <disfmarker> ?
speaker: A
text: I know Thilo and Dan Ellis are <disfmarker> are submitting something ,
speaker: A
text: but uh .
speaker: E
text: Yeah .
speaker: E
text: I <disfmarker> <vocalsound> I guess e the only thing with these <disfmarker> the Meeting Recorder and , well , <disfmarker>
speaker: E
text: So , I think , yeah <disfmarker> I think we basically gave up .
speaker: A
text: Um . <vocalsound> Now , actually for the <disfmarker> for the Aur - uh
speaker: E
text: But <disfmarker>
speaker: A
text: we do have stuff for Aurora , right ?
speaker: A
text: Because <disfmarker> because we have ano an extra month or something .
speaker: E
text: Yeah .
speaker: E
text: Yeah .
speaker: E
text: Yeah .
speaker: E
text: So .
speaker: E
text: Yeah , for sure we will do something for the special session .
speaker: A
text: Yeah .
speaker: A
text: Well , that 's fine .
speaker: A
text: So th so <disfmarker> so we have a couple <disfmarker> a couple little things on Meeting Recorder
speaker: E
text: Yeah .
speaker: E
text: Mm - hmm .
speaker: A
text: and we have <disfmarker> <vocalsound> We don't <disfmarker> we don't have to flood it with papers .
speaker: A
text: We 're not trying to prove anything to anybody .
speaker: A
text: so .
speaker: A
text: That 's fine .
speaker: A
text: Um .
speaker: A
text: Anything else ?
speaker: E
text: Yeah .
speaker: E
text: Well . So .
speaker: E
text: Perhaps the point is that we 've been working on <vocalsound> is ,
speaker: E
text: yeah , we have put the um the good VAD in the system
speaker: E
text: and <vocalsound> it really makes a huge difference .
speaker: E
text: Um .
speaker: E
text: So ,
speaker: E
text: yeah .
speaker: E
text: I think , yeah , this is perhaps one of the reason why our system was not <disfmarker> <vocalsound> not the best ,
speaker: E
text: because with the new VAD , it 's very <disfmarker> the results are similar to the France Telecom results and perhaps even better sometimes .
speaker: A
text: Hmm .
speaker: B
text: Huh .
speaker: E
text: Um . So there is this point .
speaker: E
text: Uh . The problem is that it 's very big and <vocalsound> <vocalsound> we still have to think how to <disfmarker> where to put it
speaker: E
text: and <disfmarker> <vocalsound> um ,
speaker: A
text: Mm - hmm .
speaker: E
text: because it <disfmarker> it <disfmarker>
speaker: E
text: well , this VAD
speaker: E
text: uh either some delay
speaker: E
text: and we <disfmarker> if we put it on the server side , it doesn't work ,
speaker: E
text: because on the server side features you already have LDA applied <vocalsound> from the f from the terminal side
speaker: E
text: and <vocalsound> so you accumulate the delay
speaker: E
text: so the VAD should be before the LDA
speaker: E
text: which means perhaps on the terminal side
speaker: E
text: and then smaller <vocalsound> and
speaker: A
text: So wha where did this good VAD come from ?
speaker: E
text: So .
speaker: E
text: It 's um from OGI .
speaker: E
text: So it 's the network trained <disfmarker> it 's the network with the huge amounts on hidden <disfmarker> of hidden units ,
speaker: E
text: and um nine input frames compared to the VAD that was in the proposal which has a very small amount of hidden units and fewer inputs .
speaker: A
text: This is the one they had originally ?
speaker: E
text: Yeah .
speaker: A
text: Oh .
speaker: A
text: Yeah , but they had to <pause> get rid of it because of the space ,
speaker: A
text: didn't they ?
speaker: E
text: Yeah .
speaker: E
text: So .
speaker: E
text: Yeah .
speaker: E
text: But the abso assumption is that we will be able to make a VAD that 's small and that works fine .
speaker: E
text: And . So we can <disfmarker>
speaker: A
text: Well . So that 's a problem .
speaker: A
text: Yeah .
speaker: E
text: Yeah but <disfmarker>
speaker: E
text: nnn .
speaker: A
text: But the other thing is uh to use a different VAD entirely .
speaker: A
text: I mean , uh i if <disfmarker> if there 's a <vocalsound> if <disfmarker> if <disfmarker>
speaker: A
text: I <disfmarker> I don't know what the thinking was amongst the <disfmarker> the <disfmarker> the <vocalsound> the ETSI folk
speaker: A
text: but um if everybody agreed sure let 's use this VAD and take that out of there <disfmarker>
speaker: E
text: Mm - hmm .
speaker: E
text: Mm - hmm .
speaker: E
text: They just want , apparently <disfmarker> they don't want to fix the VAD because they think there is some interaction between feature extraction and <disfmarker> and VAD or frame dropping
speaker: E
text: But they still <vocalsound> want to <disfmarker> just to give some um <vocalsound> requirement for this VAD
speaker: E
text: because it 's <disfmarker> it will not be part of <disfmarker> they don't want it to be part of the standard .
speaker: A
text: OK .
speaker: E
text: So . So it must be at least uh somewhat fixed but not completely .
speaker: E
text: So there just will be some requirements that are still not <disfmarker> uh not yet uh ready I think .
speaker: A
text: Determined .
speaker: A
text: I see .
speaker: E
text: Nnn .
speaker: A
text: But I was thinking that <disfmarker> that uh <vocalsound> s " Sure , there may be some interaction ,
speaker: A
text: but I don't think we need to be stuck on using our or OGI 's <pause> VAD .
speaker: A
text: We could use somebody else 's if it 's smaller
speaker: A
text: or <disfmarker>
speaker: E
text: Yeah .
speaker: A
text: You know , as long as it did the job .
speaker: E
text: Mm - hmm .
speaker: A
text: So that 's good .
speaker: E
text: Uh . So there is this thing .
speaker: E
text: There is
speaker: E
text: um <disfmarker>
speaker: E
text: Yeah .
speaker: E
text: Uh I designed a new <disfmarker> a new filter
speaker: E
text: because when I designed other filters with shorter delay from the LDA filters , <vocalsound> there was one filter with fif sixty millisecond delay and the other with ten milliseconds
speaker: E
text: and <vocalsound> uh Hynek suggested that both could have sixty - five sixty - s
speaker: A
text: Right .
speaker: E
text: I think it 's sixty - five .
speaker: E
text: Yeah .
speaker: A
text: Yeah .
speaker: E
text: Both should have sixty - five because <disfmarker>
speaker: E
text: Yeah .
speaker: A
text: You didn't gain anything , right ?
speaker: E
text: And . So I did that
speaker: E
text: and uh it 's running .
speaker: E
text: So , <vocalsound> let 's see what will happen .
speaker: E
text: Uh but the filter is of course closer to the reference filter .
speaker: A
text: Mm - hmm .
speaker: E
text: Mmm .
speaker: E
text: Um . Yeah .
speaker: E
text: I think <disfmarker>
speaker: A
text: So that means logically , in principle , it should be better .
speaker: A
text: So probably it 'll be worse .
speaker: E
text: Yeah
speaker: A
text: Or in the basic perverse nature uh of reality .
speaker: A
text: Yeah .
speaker: A
text: OK .
speaker: E
text: Yeah .
speaker: E
text: Sure .
speaker: C
text: Yeah .
speaker: A
text: OK .
speaker: E
text: Yeah , and then we 've started to work with this of um voiced - unvoiced stuff .
speaker: A
text: Mm - hmm .
speaker: E
text: And next week I think we will <vocalsound> perhaps try to have um a new system with uh uh MSG stream also
speaker: E
text: see what <disfmarker> what happens .
speaker: E
text: So , something that 's similar to the proposal too , but with MSG stream .
speaker: A
text: Mm - hmm .
speaker: A
text: Mm - hmm .
speaker: E
text: Mmm .
speaker: A
text: OK .
speaker: D
text: No , I w <vocalsound> I begin to play <vocalsound> with Matlab and to found some parameter robust for voiced - unvoiced decision .
speaker: D
text: But only to play .
speaker: D
text: And we <disfmarker> <vocalsound> they <disfmarker> we found that maybe w is a classical parameter , the <vocalsound> sq the variance <vocalsound> between the um FFT of the signal and the small spectrum of time <vocalsound> we <disfmarker> after the um mel filter bank .
speaker: A
text: Uh - huh .
speaker: D
text: And , well , is more or less robust .
speaker: D
text: Is good for clean speech .
speaker: D
text: Is quite good <vocalsound> for noisy speech .
speaker: A
text: Huh ?
speaker: A
text: Mm - hmm .
speaker: D
text: but um we must to have bigger statistic with TIMIT ,
speaker: A
text: Mm - hmm .
speaker: D
text: and is not ready yet to use on ,
speaker: A
text: Yeah .
speaker: D
text: well , I don't know .
speaker: A
text: Yeah .
speaker: E
text: Yeah .
speaker: E
text: So , basically we wa want to look at something like the ex the ex excitation signal and <disfmarker>
speaker: A
text: Right .
speaker: D
text: Mm - hmm .
speaker: E
text: which are the variance of it and <disfmarker>
speaker: D
text: I have here .
speaker: D
text: I have here for one signal , for one frame .
speaker: E
text: Mmm .
speaker: A
text: Yeah .
speaker: D
text: The <disfmarker> the mix of the two , noise and unnoise , and the signal is this .
speaker: A
text: Uh - huh .
speaker: D
text: Clean , and this noise .
speaker: A
text: Uh .
speaker: D
text: These are the two <disfmarker>
speaker: D
text: the mixed , the big signal is for clean .
speaker: A
text: Well , I 'm s uh <disfmarker>
speaker: A
text: There 's <disfmarker>
speaker: A
text: None of these axes are labeled ,
speaker: A
text: so I don't know what this <disfmarker>
speaker: A
text: What 's this axis ?
speaker: D
text: Uh this is uh <disfmarker> this axis is <vocalsound> nnn , " frame " .
speaker: A
text: Frame .
speaker: D
text: Mm - hmm .
speaker: A
text: And what 's th what this ?
speaker: D
text: Uh , this is uh energy , log - energy of the spectrum .
speaker: D
text: Of the
speaker: D
text: this is the variance ,
speaker: D
text: the difference <nonvocalsound> between the spectrum of the signal and FFT of each frame of the signal
speaker: D
text: and this mouth spectrum of time after the f may fit for the two ,
speaker: A
text: For this one .
speaker: A
text: For the noi
speaker: D
text: this big , to here , they are to signal .
speaker: D
text: This is for clean and this is for noise .
speaker: A
text: Oh .
speaker: A
text: There 's two things on the same graph .
speaker: D
text: Yeah .
speaker: D
text: I don't know .
speaker: D
text: I <disfmarker> I think that I have d another graph ,
speaker: D
text: but I 'm not sure .
speaker: E
text: Yeah .
speaker: A
text: So w which is clean and which is noise ?
speaker: E
text: I think the lower one is noise .
speaker: D
text: The lower is noise and the height is clean .
speaker: A
text: OK .
speaker: A
text: So it 's harder to distinguish
speaker: D
text: It 's height .
speaker: A
text: but it <disfmarker> but it g
speaker: E
text: Yeah .
speaker: A
text: with noise of course
speaker: A
text: but <disfmarker> but <disfmarker>
speaker: D
text: Oh . I must to have .
speaker: A
text: Uh .
speaker: D
text: Pity , but I don't have two different
speaker: A
text: And presumably when there 's a <disfmarker> a <disfmarker>
speaker: E
text: So this should the <disfmarker> the <disfmarker> the t voiced portions .
speaker: A
text: Uh - huh .
speaker: D
text: Yeah , it is the height is voiced portion .
speaker: E
text: The p the peaks should be voiced portion .
speaker: D
text: And this is the noise portion .
speaker: A
text: Uh - huh .
speaker: D
text: And this is more or less like this .
speaker: D
text: But I meant to have see @ @ two <disfmarker> two the picture .
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: D
text: This is , for example , for one frame .
speaker: A
text: Yeah
speaker: D
text: the <disfmarker> the spectrum of the signal .
speaker: D
text: And this is the small version of the spectrum after ML mel filter bank .
speaker: A
text: Yeah .
speaker: A
text: And this is the difference ?
speaker: D
text: And this is
speaker: D
text: I don't know .
speaker: D
text: This is not the different .
speaker: D
text: This is trying to obtain <vocalsound> with LPC model the spectrum
speaker: D
text: but using Matlab without going factor and s
speaker: A
text: No pre - emphasis ?
speaker: A
text: Yeah .
speaker: D
text: Not pre - emphasis .
speaker: D
text: Nothing .
speaker: A
text: Yeah so it 's <disfmarker>
speaker: D
text: And the <disfmarker>
speaker: A
text: doesn't do too well there .
speaker: D
text: I think that this is good .
speaker: D
text: This is quite similar .
speaker: D
text: this is <disfmarker> <vocalsound> this is another frame .
speaker: D
text: ho how I obtained the <vocalsound> envelope , <nonvocalsound> this envelope , with the mel filter bank .
speaker: A
text: Right .
speaker: A
text: So now I wonder <disfmarker>
speaker: A
text: I mean , do you want to <disfmarker>
speaker: A
text: I know you want to get at something orthogonal from what you get with the smooth spectrum
speaker: A
text: Um . But if you were to really try and get a voiced - unvoiced , do you <disfmarker> do you want to totally ignore that ?
speaker: A
text: I mean , do you <disfmarker> do you <disfmarker>
speaker: A
text: I mean , clearly a <disfmarker> a very big <disfmarker> very big cues <vocalsound> for voiced - unvoiced come from uh spectral slope and so on ,
speaker: E
text: Mm - hmm .
speaker: A
text: right ?
speaker: A
text: Um .
speaker: E
text: Yeah .
speaker: E
text: Well , this would be <disfmarker> this would be perhaps an additional parameter ,
speaker: E
text: simply isn't <disfmarker>
speaker: A
text: Yeah .
speaker: A
text: I see .
speaker: E
text: Yeah .
speaker: D
text: Yeah because when did noise clear <nonvocalsound> in these section is clear
speaker: E
text: Uh .
speaker: A
text: Mm - hmm .
speaker: D
text: if s @ @ <nonvocalsound> val value is indicative that is a voice frame and it 's low values
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: A
text: Well , you probably want <disfmarker>
speaker: A
text: I mean , <vocalsound> certainly if <vocalsound> you want to do good voiced - unvoiced detection , you need a few features .
speaker: A
text: Each <disfmarker> each feature is <vocalsound> by itself not enough .
speaker: A
text: But , you know , people look at <disfmarker> at slope and <vocalsound> uh first auto - correlation coefficient , divided by power .
speaker: E
text: Mmm .
speaker: A
text: Or <disfmarker> or uh
speaker: A
text: um
speaker: A
text: there 's uh <disfmarker>
speaker: A
text: I guess we prob probably don't have enough computation to do a simple pitch detector or something ?
speaker: A
text: I mean with a pitch detector you could have a <disfmarker> <vocalsound> have a <disfmarker> an estimate of <disfmarker> of what the <disfmarker>
speaker: E
text: Mmm .
speaker: A
text: Uh .
speaker: A
text: Or maybe you could you just do it going through the P FFT 's figuring out some um probable <vocalsound> um harmonic structure .
speaker: A
text: Right .
speaker: E
text: Mmm .
speaker: A
text: And <disfmarker> and uh .
speaker: D
text: you have read up and <disfmarker> you have a paper , <vocalsound> the paper that you s give me yesterday .
speaker: E
text: Oh , yeah .
speaker: D
text: they say that yesterday <vocalsound> they are some <nonvocalsound> problem
speaker: E
text: But <disfmarker>
speaker: E
text: Yeah , but it 's not <disfmarker>
speaker: E
text: it 's ,
speaker: E
text: yeah , it 's <disfmarker> it 's another problem .
speaker: D
text: and the <disfmarker>
speaker: E
text: Yeah
speaker: D
text: Is another problem .
speaker: E
text: Um .
speaker: E
text: Yeah , there is th this fact actually .
speaker: E
text: If you look at this um spectrum ,
speaker: A
text: Yeah .
speaker: E
text: What 's this again ?
speaker: E
text: Is it <vocalsound> the mel - filters ?
speaker: D
text: Yeah like this .
speaker: E
text: Yeah .
speaker: D
text: Of kind like this .
speaker: E
text: OK .
speaker: E
text: So the envelope here is the output of the mel - filters
speaker: A
text: Mm - hmm .
speaker: E
text: and what we clearly see is that in some cases ,
speaker: E
text: and it clearly appears here ,
speaker: E
text: and the <disfmarker> the harmonics are resolved by the f
speaker: E
text: Well , there are still appear after mel - filtering ,
speaker: A
text: Mm - hmm .
speaker: E
text: and it happens <vocalsound> for high pitched voice because the width of the lower frequency mel - filters <vocalsound> is sometimes even smaller than the pitch .
speaker: A
text: Yeah .
speaker: E
text: It 's around one hundred , one hundred and fifty hertz <vocalsound> Nnn .
speaker: A
text: Right .
speaker: E
text: And so what happens is that this uh , add additional variability to this envelope
speaker: E
text: and <vocalsound> <vocalsound> um
speaker: A
text: Yeah .
speaker: E
text: so we were thinking to modify the mel - spectrum to have something that <disfmarker> that 's smoother on low frequencies .
speaker: A
text: That 's as <disfmarker> as a separate thing .
speaker: E
text: i
speaker: A
text: Yeah .
speaker: E
text: Yeah .
speaker: E
text: This is a separate thing .
speaker: D
text: Yeah .
speaker: A
text: Separate thing ?
speaker: A
text: Yeah .
speaker: E
text: And .
speaker: A
text: Yeah .
speaker: A
text: Maybe so .
speaker: A
text: Um .
speaker: A
text: Yeah .
speaker: A
text: So , what <disfmarker>
speaker: A
text: Yeah .
speaker: A
text: What I was talking about was just , starting with the FFT
speaker: A
text: you could <disfmarker> you could uh do a very rough thing to estimate <disfmarker> estimate uh pitch .
speaker: E
text: Yeah .
speaker: E
text: Mm - hmm .
speaker: A
text: And uh uh , given <disfmarker> you know , given that , uh <vocalsound> you could uh uh come up with some kind of estimate of how much of the low frequency energy was <disfmarker> was explained by <disfmarker> <vocalsound> by uh uh those harmonics .
speaker: E
text: Mm - hmm .
speaker: A
text: Uh .
speaker: A
text: It 's uh a variant on what you 're s what you 're doing .
speaker: A
text: The <disfmarker> I mean , the <disfmarker> the <vocalsound> the mel does give a smooth thing .
speaker: A
text: But as you say it 's not that smooth here .
speaker: A
text: And <disfmarker> and so if you <disfmarker> <vocalsound> if you just you know subtracted off uh your guess of the harmonics then something like this would end up with <vocalsound> quite a bit lower energy in the first fifteen hundred hertz or so
speaker: A
text: and <disfmarker> and our first kilohertz , even .
speaker: E
text: Mm - hmm .
speaker: A
text: And um <vocalsound> if was uh noisy , the proportion that it would go down would be
speaker: E
text: Mm - hmm .
speaker: A
text: if it was <disfmarker> if it was unvoiced or something .
speaker: A
text: So you oughta be able to <vocalsound> pick out voiced segments .
speaker: A
text: At least it should be another <disfmarker> another cue .
speaker: E
text: Mm - hmm .
speaker: A
text: So . <vocalsound> Anyway .
speaker: A
text: OK ?
speaker: A
text: That 's what 's going on .
speaker: A
text: Uh .
speaker: A
text: What 's up with you ?
speaker: B
text: Um <vocalsound> our t I went to <vocalsound> talk with uh Mike Jordan this <disfmarker> this week
speaker: A
text: Mm - hmm .
speaker: B
text: um <nonvocalsound> and uh <vocalsound> shared with him the ideas about um <vocalsound> extending the Larry Saul work
speaker: B
text: and um I asked him some questions about factorial H M
speaker: B
text: so like later down the line when <vocalsound> we 've come up with these <disfmarker> these feature detectors , how do we <disfmarker> <vocalsound> how do we uh <vocalsound> you know , uh model the time series that <disfmarker> that happens
speaker: B
text: um <vocalsound> <vocalsound> and <vocalsound> and we talked a little bit about <vocalsound> factorial H M Ms and how <vocalsound> um when you 're doing inference <disfmarker> or w when you 're doing recognition , there 's like simple Viterbi stuff that you can do for <disfmarker> <vocalsound> for these H M
speaker: B
text: and <vocalsound> the uh <disfmarker> <vocalsound> the great advantages that um a lot of times the factorial H M Ms don't <vocalsound> um <vocalsound> don't over - alert the problem
speaker: B
text: there they have a limited number of parameters and they focus directly on <disfmarker> <vocalsound> on uh the sub - problems at hand
speaker: B
text: so <vocalsound> you can imagine <vocalsound> um <vocalsound> five or so parallel <vocalsound> um features um transitioning independently
speaker: B
text: and then <vocalsound> at the end you <disfmarker> you uh couple these factorial H M Ms with uh <disfmarker> <vocalsound> with uh undirected links um based on <disfmarker> <vocalsound> based on some more data .
speaker: A
text: Hmm .
speaker: B
text: So he <disfmarker> he seemed <disfmarker> he seemed like really interested in <disfmarker> <vocalsound> in um <disfmarker> in this
speaker: B
text: and said <disfmarker> said this is <disfmarker> this is something very do - able and can learn a lot
speaker: B
text: and um
speaker: B
text: yeah , I 've just been <vocalsound> continue reading um about certain things .
speaker: A
text: Mm - hmm .
speaker: B
text: um
speaker: B
text: thinking of maybe using um <vocalsound> um m modulation spectrum stuff to <vocalsound> um <disfmarker> as features um also in the <disfmarker> in the sub - bands
speaker: A
text: Mm - hmm .
speaker: B
text: because <vocalsound> it seems like <vocalsound> the modulation um spectrum tells you a lot about the intelligibility of <disfmarker> of certain um words and stuff
speaker: B
text: So , um .
speaker: B
text: Yeah .
speaker: B
text: Just
speaker: B
text: that 's about it .
speaker: A
text: OK .
speaker: C
text: OK .
speaker: C
text: And
speaker: C
text: um
speaker: C
text: so I 've been looking at Avendano 's work
speaker: C
text: and um
speaker: C
text: uh I 'll try to write up in my next stat status report a nice description of <vocalsound> what he 's doing ,
speaker: C
text: but it 's <disfmarker> it 's an approach to deal with <vocalsound> reverberation or that <disfmarker> the aspect of his work that I 'm interested in
speaker: C
text: the idea is that um <vocalsound> <vocalsound> <vocalsound> normally an analysis frames are um <vocalsound> too short to encompass reverberation effects um in full .
speaker: C
text: You miss most of the reverberation tail in a ten millisecond window
speaker: C
text: and so <vocalsound> <vocalsound> you <disfmarker> you 'd like it to be that <vocalsound> um <vocalsound> the reverberation responses um simply convolved um in ,
speaker: C
text: but it 's not really with these ten millisecond frames
speaker: C
text: cuz you j
speaker: C
text: But if you take , say , a two millisecond <vocalsound> um window <disfmarker>
speaker: C
text: I 'm sorry
speaker: C
text: a two second window
speaker: C
text: then in a room like this , most of the reverberation response <vocalsound> is included in the window
speaker: C
text: and the <disfmarker> then it um <vocalsound> then things are l more linear .
speaker: C
text: It is <disfmarker> it is more like the reverberation response is simply c convolved
speaker: C
text: and um <disfmarker> <vocalsound> and you can use channel normalization techniques <vocalsound> like
speaker: C
text: uh in his thesis he 's assuming that the reverberation response is fixed .
speaker: C
text: He just does um <vocalsound> mean subtraction ,
speaker: C
text: which is like removing the DC component of the modulation spectrum
speaker: C
text: and <vocalsound> that 's supposed to d um deal <disfmarker> uh deal pretty well with the um reverberation
speaker: C
text: and um <vocalsound> the neat thing is you can't take these two second frames and feed them to a speech recognizer
speaker: C
text: um <vocalsound> so he does this <vocalsound> um <vocalsound> method training trading the um <vocalsound> the spectral resolution for time resolution <vocalsound> and um <vocalsound> come ca uh synthesizes a new representation which is with say ten second frames but a lower s um <vocalsound> frequency resolution .
speaker: C
text: So I don't really know the theory .
speaker: C
text: I guess it 's <disfmarker> these are called " time frequency representations "
speaker: C
text: and h he 's making the <disfmarker> the time sh um finer grained and the frequency resolution um less fine grained .
speaker: E
text: Mm - hmm .
speaker: C
text: s so I 'm <disfmarker> I guess my first stab actually in continuing <vocalsound> his work is to um <vocalsound> re - implement this <disfmarker> this thing which um <vocalsound> changes the time and frequency resolutions
speaker: C
text: cuz he doesn't have code for me .
speaker: C
text: So that that 'll take some reading about the theory .
speaker: C
text: I don't really know the theory .
speaker: E
text: Mm - hmm .
speaker: C
text: Oh , and um , <vocalsound> another f first step is
speaker: C
text: um ,
speaker: C
text: so the <disfmarker> the way I want to extend his work is make it able to deal with a time varying reverberation response
speaker: C
text: um <vocalsound> and um
speaker: C
text: we don't really know <vocalsound> how fast the um <disfmarker> the reverberation response is varying the Meeting Recorder data
speaker: C
text: um so um <vocalsound> we <disfmarker> we have this um block least squares um imp echo canceller implementation
speaker: C
text: and um <vocalsound> I want to try <vocalsound> finding <vocalsound> the <disfmarker> the response , say , between a near mike and the table mike for someone using the echo canceller
speaker: C
text: and looking at the echo canceller taps and then <vocalsound> see how fast that varies <vocalsound> from block to block .
speaker: E
text: Mm - hmm .
speaker: C
text: That should give an idea of how fast the reverberation response is changing .
speaker: E
text: Mm - hmm .
speaker: A
text: OK .
speaker: A
text: Um . I think we 're <vocalsound> sort of done .
speaker: E
text: Yeah .
speaker: A
text: So let 's read our digits and go home .
speaker: C
text: Um .
speaker: C
text: S so um
speaker: C
text: y you do <disfmarker> I think you read some of the <disfmarker> the zeros as O 's and some as zeros .
speaker: A
text: Yeah .
speaker: C
text: Is there a particular way we 're supposed to read them ?
speaker: E
text: There are only zeros here .
speaker: E
text: Well .
speaker: A
text: No . " O " <disfmarker> " O " <disfmarker> " O "
speaker: A
text: " O " <disfmarker> " O " <disfmarker> " O " and " zero " are two ways that we say that digit .
speaker: E
text: Eee .
speaker: E
text: Yeah .
speaker: A
text: So it 's <disfmarker>
speaker: E
text: But <disfmarker>
speaker: B
text: Ha !
speaker: A
text: so it 's <disfmarker> i
speaker: E
text: Perhaps in the sheets there should be another sign for the <disfmarker>
speaker: E
text: if we want to <disfmarker> the <disfmarker> the guy to say " O "
speaker: E
text: or
speaker: A
text: No .
speaker: A
text: I mean . I think people will do what they say .
speaker: E
text: It 's <disfmarker>
speaker: E
text: Yeah .
speaker: A
text: It 's OK .
speaker: E
text: OK .
speaker: C
text: Alright .
speaker: A
text: I mean in digit recognition we 've done before , you have <disfmarker> you have two pronunciations for that value , " O " and " zero " .
speaker: C
text: OK .
speaker: E
text: But it 's perhaps more difficult for the people to prepare the database then , if <disfmarker>
speaker: E
text: because here you only have zeros
speaker: A
text: No , they just write <disfmarker>
speaker: E
text: and <disfmarker> and people pronounce " O " or zero <disfmarker>
speaker: A
text: they <disfmarker> they write down OH .
speaker: A
text: or they write down ZERO
speaker: A
text: a and they <disfmarker> and they each have their own pronunciation .
speaker: E
text: Yeah but if the sh the sheet was prepared with a different sign for the " O " .
speaker: A
text: But people wouldn't know what that wa
speaker: A
text: I mean <vocalsound> there is no convention for it .
speaker: E
text: OK .
speaker: E
text: Yeah .
speaker: E
text: OK .
speaker: A
text: See . I mean , you 'd have to tell them <vocalsound> " OK when we write this , say it tha " ,
speaker: A
text: you know ,
speaker: A
text: and you just <disfmarker> They just want people to read the digits as you ordinarily would
speaker: E
text: Mm - hmm .
speaker: E
text: Yeah .
speaker: E
text: Yep .
speaker: A
text: and <disfmarker> and people say it different ways .
speaker: C
text: OK .
speaker: C
text: Is this a change from the last batch of <disfmarker> of um forms ?
speaker: C
text: Because in the last batch it was spelled out which one you should read .
speaker: E
text: Yeah , it was orthographic ,
speaker: E
text: so .
speaker: A
text: Yes .
speaker: A
text: That 's right .
speaker: A
text: It was <disfmarker> it was spelled out , and they decided they wanted to get at more the way people would really say things .
speaker: C
text: Oh . OK .
speaker: A
text: That 's also why they 're <disfmarker> they 're bunched together in these different groups .
speaker: C
text: OK .
speaker: A
text: So <disfmarker> so it 's <disfmarker>
speaker: A
text: Yeah .
speaker: A
text: So it 's <disfmarker> it 's <disfmarker>
speaker: A
text: Everything 's fine .
speaker: C
text: OK .
speaker: A
text: OK .
speaker: A
text: Actually , let me just s since <disfmarker> since you brought it up , I was just <disfmarker> it was hard not to be self - conscious about that when it <vocalsound> after we <disfmarker> since we just discussed it .
speaker: A
text: But I realized that <disfmarker> that um <vocalsound> when I 'm talking on the phone , certainly , and <disfmarker> and saying these numbers , <vocalsound> I almost always say zero .
speaker: A
text: And uh <disfmarker> cuz <disfmarker> because uh i it 's two syllables .
speaker: A
text: It 's <disfmarker> it 's more likely they 'll understand what I said .
speaker: A
text: So that <disfmarker> that <disfmarker> that 's the habit I 'm in ,
speaker: A
text: but some people say " O " and <disfmarker>
speaker: B
text: Yeah I normally say " O " cuz it 's easier to say .
speaker: A
text: Yeah it 's shorter .
speaker: A
text: Yeah .
speaker: A
text: So it 's <disfmarker> So . <vocalsound> So uh .
speaker: B
text: " O "
speaker: A
text: Now , don't think about it .
speaker: B
text: Oh , no !
speaker: A
text: OK .
speaker: A
text: We 're done .

Meeting summary:
text: The Main purpose of the meeting of ICSI's Meeting Recorder Group at Berkeley was to discuss the recent progress of it's members.
text: This includes reports on the progress of the groups main digit recogniser project, with interest on voice-activity detectors and voiced/unvoiced detection, work on acoustic feature detection, and research into dealing with reverberation.
text: There was also talk of comparing different recognition systems and training datasets, and a discussion of the pronunciation of the digit zero for the recording at the end of the meeting.
text: In his next status report, me026 will summarise the work he has been researching.
text: The digit recognition system is still not working well enough, they must get better results if they want to publish and be noticed.
text: They have not really made many improvements, which may be due to their comparatively small training set, or the conditions the data is recorded under.
text: The new VAD is quite a large network, and adds a delay to the process.
text: This caused OGI to drop it, though speaker mn007 is assuming that a smaller and equally effective system can be developed.
text: The alternative is to get yet another VAD form somewhere else, though it's not clear if they will even be required in the final system.
text: There are some problems with the voiced/unvoiced feature detection, because some pitches are slipping through the filtering.
text: The group have been comparing their recognition system to a few others, and theirs has not come off favourably.
text: There could be many reasons for this, including smaller training set, more realistic data, or older technology.
text: Speaker mn007 has put the best voice activity detector into the system, to great improvements along with designing new filters that run at the correct latency.
text: Speaker fn002 has started to find parameters for voiced/unvoiced feature detection, and has found some classic ones, although there are other things she wishes to look at.
text: Me013 offers a few ideas of simple things she may want to try, as he is not confident with everything she is trying.
text: Speaker me006 is continuing with the idea of extending work on acoustic feature detection.
text: He is continuing to read, and has discussed the suitability of factorial HMMs with a colleague.
text: Speaker me026 has been learning more about previous work on reverberation, and is ready to start with a re-implementation of the theory.
text: From there he wants to extend the work to look at time-varying reverb.