B: I think for two years we were two months , uh , away from being done .
A: And what was that , Morgan ?
A: What project ?
B: Uh , the , uh , TORRENT chip .
A: Oh .
B: Yeah .
B: We were two B: we were C: Yeah .
B: Uh , uh , we went through it Jim and I went through old emails at one point
B: and and for two years there was this thing saying , yeah , we 're we 're two months away from being done .
B: It was very very believable schedules , too .
B: I mean , we went through and with the schedules and we A: It was true for two years .
B: Yeah .
B: Oh , yeah .
B: It was very true .
A: So , should we just do the same kind of deal where we go around and do , uh , status report kind of things ?
A: OK .
A: And I guess when Sunil gets here he can do his last or something .
C: Mm - hmm .
A: So .
B: Yeah .
B: So we probably should wait for him to come before we do his .
A: OK .
F: OK .
A: That 's a good idea .
B: Yeah .
B: Yeah .
A: Any objection ?
A: Do y
B: All in favor
A: OK ,
A: M
A: Do you want to start , Morgan ?
A: Do you have anything ,
A: or ?
B: Uh , I don't do anything .
B: I B: No , I mean , I I 'm involved in discussions with with people about what they 're doing ,
B: but I think they 're since they 're here , they can talk about it themselves .
F: OK .
F: So should I go so that , uh ,
A: Yeah .
A: Why don't you go ahead , Barry ?
F: you 're gonna talk about Aurora stuff , per se ?
A: OK .
F: OK .
F: Um .
F: Well , this past week I 've just been , uh , getting down and dirty into writing my my proposal .
F: So ,
F: um F: Mmm .
F: I just finished a section on , uh on talking about these intermediate categories that I want to classify , um , as a as a middle step .
F: And , um ,
F: I hope to hope to get this , um a full rough draft done by , uh , Monday so I can give it to Morgan .
A: When is your , uh , meeting ?
F: Um , my meeting
A: Yeah .
F: with , uh ?
F: Oh , oh , you mean the the quals .
A: The quals .
A: Yeah .
F: Uh , the quals are happening in July twenty - fifth .
A: Oh .
A: Soon .
F: Yeah .
F: D - Day .
A: Uh - huh .
A: Yeah .
F: Uh - huh .
A: So , is the idea you 're going to do this paper
A: and then you pass it out to everybody ahead of time
A: and ?
F: Right , right .
F: So , y you write up a proposal , and give it to people ahead of time ,
F: and you have a short presentation .
F: And , um ,
F: and then , um then everybody asks you questions .
A: Hmm .
F: Yeah .
A: I remember now .
F: Yep .
F: So , um .
A: Have you d ?
F: Y s
A: I was just gonna ask , do you want to say any a little bit about it ,
A: or ?
F: Oh .
F: Uh , a little bit about ?
A: Mmm .
A: Wh - what you 're what you 're gonna A: You said you were talking about the , uh , particular features that you were looking at ,
F: Oh ,
F: the the A: or F: Right .
F: Well , I was ,
F: um ,
F: I think one of the perplexing problems is ,
F: um ,
F: for a while I was thinking that I had to come up with a complete set of intermediate features in intermediate categories to to classify right away .
F: But what I 'm thinking now is , I would start with with a reasonable set .
F: Something something like , um , um like , uh , re regular phonetic features ,
F: just to just to start off that way .
F: And do some phone recognition .
F: Um , build a system that , uh , classifies these , um these feat uh , these intermediate categories using , uh , multi - band techniques .
F: Combine them and do phon phoneme recognition .
F: Look at F: 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 .
F: That would that would reduce certain confusions over other confusions .
F: And then and then reiterate .
F: Um , build the intermediate classifiers .
F: Uh , do phoneme recognition .
F: Look at the errors .
F: And then postulate new or remove , um , intermediate categories .
F: And then do it again .
A: So you 're gonna use TIMIT ?
F: Um , for that for that part of the the process , yeah , I would use TIMIT .
A: Mm - hmm .
F: And ,
F: um ,
F: then F: after after , uh , um , doing TIMIT .
F: Right ?
F: Um , that 's  that 's , um that 's just the ph the phone recognition task .
A: Mm - hmm .
A: Yeah .
F: Uh , I wanted to take a look at , um , things that I could model within word .
F: So , I would mov I would then shift the focus to , um , something like Schw - Switchboard ,
F: uh , where I 'd I would be able to , um to model , um , intermediate categories that span across phonemes ,
F: not just within the phonemes , themselves ,
A: Mm - hmm .
F: um ,
F: and then do the same process there , um , on on a large vocabulary task like Switchboard .
F: Uh ,
F: and for that F: for that part I would I 'd use the SRI recognizer
F: since it 's already set up for for Switchboard .
F: And I 'd run some some sort of tandem - style processing with , uh , my intermediate classifiers .
A: Oh . So that 's why you were interested in getting your own features into the SRI files .
F: Yeah . That 's why I I was asking about that .
A: Yeah .
F: Yeah .
A: Yeah .
F: Um ,
F: and
F: I guess that 's that 's it .
F: Any any questions ?
A: Sounds good .
A: So you just have a few more weeks ,
A: huh ?
F: Um , yeah .
F: A few more .
A: It 's about a month from now ?
F: It 's a it 's a month and and a week .
A: Yeah .
F: Yeah .
A: So , uh , you want to go next , Dave ?
A: And we 'll do E: Oh .
E: OK ,
E: sure .
E: So , um ,
E: last week I finally got results from the SRI system about this mean subtraction approach .
E: And , um , we we got an improvement , uh , in word error rate , training on the TI - digits data set and testing on Meeting Recorder digits of , um , six percent to four point five percent ,
E: um , on the n on the far - mike data
E: using PZM F ,
E: but , um , the near - mike performance worsened , um , from one point two percent to two point four percent .
E: And , um ,
E: wh why would that be , um , considering that we actually got an improvement in near - mike performance using HTK ?
E: And so ,
E: uh , with some input from , uh , Andreas , I have a theory in two parts .
E: Um ,
E: first of all HTK sorry , SR - the SRI system is doing channel adaptation ,
E: and so HTK wasn't .
E: Um ,
E: so this ,
E: um E: This mean subtraction approach will do a kind of channel normalization
E: and so that might have given the HTK use of it a boost that wouldn't have been applied in the SRI case .
E: And also , um , the Andreas pointed out the SRI system is using more parameters .
E: It 's got finer - grained acoustic models .
E: So those finer - grained acoustic models could be more sensitive to the artifacts in the re - synthesized audio .
E: Um .
E: And me and Barry were listening to the re - synthesized audio
E: and sometimes it seems like you get of a bit of an echo of speech in the background .
E: And so that seems like it could be difficult for training ,
E: cuz you could have different phones lined up with a different foreground phone , um , depending on the timing of the echo .
E: So , um ,
E: I 'm gonna try training on a larger data set ,
E: and then , eh , the system will have seen more examples o of these artifacts and hopefully will be more robust to them .
E: So I 'm planning to use the Macrophone set of , um , read speech ,
E: and , um E: Hmm .
B: I had another thought just now ,
B: which is , uh , remember we were talking before about we were talking in our meeting about , uh , this stuff that some of the other stuff that Avendano did ,
B: where they were , um , getting rid of low - energy sections ?
B: Um ,
B: uh ,
B: if you B: if you did a high - pass filtering , as Hirsch did in 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 an all - positive power spectrum you get some negative values ,
B: and you gotta figure out what to do with them if you 're gonna continue treating this as a power spectrum .
B: So , what what Hirsch did was , uh , set them to zero B: set the negative values to zero .
B: So if you imagine a a waveform that 's all positive ,
B: which is the time trajectory of energy ,
B: um , and , uh , shifting it downwards , and then getting rid of the negative parts , that 's essentially throwing away the low - energy things .
B: And it 's the low - energy parts of the speech where the reverberation is most audible .
B: You know , you have the reverberation from higher - energy things showing up in B: So in this case you have some artificially imposed reverberation - like thing .
B: I mean , you 're getting rid of some of the other effects of reverberation ,
B: but because you have these non - causal windows , you 're getting these funny things coming in ,
B: uh , at n
B: And ,
B: um ,
B: what if you did ?
B: I mean , there 's nothing to say that the the processing for this re - synthesis has to be restricted to trying to get it back to the original , according to some equation .
E: Uh - huh .
B: I mean , you also could , uh , just try to make it nicer .
E: Mm - hmm .
B: And one of the things you could do is , you could do some sort of VAD - like thing
B: and you actually could take very low - energy sections and set them to some some , uh , very low or or near zero value .
E: Uh - huh .
B: I mean ,
B: uh , I 'm just saying if in fact it turns out that that these echoes that you 're hearing are , uh B: or pre - echoes ,
B: whichever they are B: are are , uh , part of what 's causing the problem , you actually could get rid of them .
E: Uh - huh .
B: Be pretty simple .
E: OK .
B: I mean , you do it in a pretty conservative way
B: so that if you made a mistake you were more likely to keep in an echo than to throw out speech .
E: Hmm .
G: Um , what is the reverberation time like there ?
E: In thi in this room ?
G: On , uh , the the one what the s in the speech that you are you are using like ?
E: Uh E: Y Yeah .
E: I I I I don't know .
B: So , it 's this room .
G: It 's , uh G: Oh , this room ?
B: It 's it 's this room .
G: OK .
B: So B: so it 's these are just microphone B: this micro close microphone and a distant microphone , he 's doing these different tests on .
F: Oh .
B: Uh , we should do a measurement in here .
B: I g think we never have .
B: I think it 's I would guess , uh , point seven , point eight seconds f uh , R T
F: Hmm !
B: something like that ?
G: Mm - hmm .
B: But it 's you know , it 's this room .
B: So .
G: OK .
G: Mm - hmm .
B: Uh .
B: But the other thing is , he 's putting in w
B: I was using the word " reverberation " in two ways .
B: He 's also putting in , uh , a B: he 's taking out some reverberation ,
B: but he 's putting in something ,
B: because he has averages over multiple windows stretching out to twelve seconds ,
B: which are then being subtracted from the speech .
B: And since , you know , what you subtract , sometimes you 'll be you 'll be subtracting from some larger number
B: and sometimes you won't .
G: Mm - hmm . Mm - hmm .
B: And B: So you can end up with some components in it that are affected by things that are seconds away .
B: Uh , and if it 's a low energy compo portion , you might actually hear some funny things .
G: Yeah .
E: 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 .
E: So I was wondering , is it possible that one reason it helped with the Aurora baseline system is just as a kind of gain control ?
E: Cuz some of the PZM signals sound pretty quiet if you don't amplify them .
C: Mm - hmm .
C: I don't see why why your signal is louder after processing ,
C: because yo
E: Yeah . I don't know why - y , uh , either .
C: Yeah .
B: I don't think just multiplying the signal by two would have any effect .
C: Mm - hmm .
E: Oh ,
E: OK .
B: Yeah .
B: I mean , I think if you really have louder signals , what you mean is that you have better signal - to - noise ratio .
C: Well , well B: So if what you 're doing is improving the signal - to - noise ratio , then it would be better .
C: Mm - hmm .
B: But just it being bigger if with the same signal - to - noise ratio E: It w i i it wouldn't affect things .
C: Yeah .
E: OK .
B: No .
C: Well , the system is use the absolute energy , so it 's a little bit dependent on on the signal level .
C: But , not so much , I guess .
B: Well , yeah .
B: But it 's trained and tested on the same thing .
C: Mmm .
C: Mm - hmm .
B: So if the if the if you change in both training and test , the absolute level by a factor of two , it will n have no effect .
C: Yeah .
A: Did you add this data to the training set , for the Aurora ?
A: Or you just tested on this ?
E: Uh E: Um .
E: Did I w what ?
E: Sorry ?
A: Well , Morgan was just saying that , uh , as long as you do it in both training and testing , it shouldn't have any effect .
E: Yeah .
A: But I I was sort of under the impression that you just tested with this data .
E: I E: I b
A: You didn't train it also .
E: I E: Right .
E: I trained on clean TI - digits .
E: I I did the mean subtraction on clean TI - digits .
E: But I didn't I 'm not sure if it made the clean ti TI - digits any louder .
B: Oh , I see .
E: I only remember noticing it made the , um , PZM signal louder .
B: OK .
B: Well , I don't understand then .
B: Yeah .
E: Huh .
E: I don't know .
E: If it 's if it 's like , if it 's trying to find a a reverberation filter , it could be that this reverberation filter is making things quieter .
E: And then if you take it out that taking it out makes things louder .
E: I mean .
B: Uh , no .
B: I mean , uh , there 's there 's nothing inherent about removing if you 're really removing ,
E: Nuh - huh .
E: The mean .
B: uh , r uh , then I don't see how that would make it louder .
E: OK .
E: Yeah ,
E: I see .
E: Yeah .
E: OK .
B: So it might be just some E: So I should maybe listen to that stuff again .
B: Yeah .
B: It might just be some artifact of the processing that that , uh , if you 're B: Uh , yeah .
E: Oh . OK .
B: I don't know .
C: Eh
A: I wonder if there could be something like , uh for s for the PZM data ,
A: uh , you know , if occasionally , uh , somebody hits the table or something , you could get a spike .
A: Uh .
A: I 'm just wondering if there 's something about the , um you know , doing the mean normalization where , uh , it it could cause you to have better signal - to - noise ratio .
A: Um .
B: Well , you know , there is this .
B: Wait a minute .
B: It it i maybe B: i
B: If , um B: Subtracting the the mean log spectrum is is is like dividing by the spectrum .
B: So , depending what you divide by , if your if s your estimate is off and sometimes you 're you 're you 're getting a small number , you could make it bigger .
A: Mm - hmm .
E: Mm - hmm .
B: So , it 's it 's just a a question of B: there 's It it could be that there 's some normalization that 's missing ,
B: or something
E: Mm - hmm .
B: to make it B: Uh , y you 'd think it shouldn't be larger ,
B: but maybe in practice it is .
E: Hmm .
B: That 's something to think about .
B: I don't know .
C: I had a question about the system the SRI system .
C: So , you trained it on TI - digits ?
C: But except this , it 's exactly the same system as the one that was tested before and that was trained on Macrophone .
C: Right ?
C: So on TI - digits it gives you one point two percent error rate
C: and on Macrophone it 's still O point eight .
C: Uh , but is it exactly the same system ?
E: Uh .
E: I think so .
E: 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 .
C: Hmm .
C: Mm - hmm .
C: So you use VTL - uh , vocal tract length normalization and , um , like MLLR transformations also ,
E: Mm - hmm .
C: and E: That 's B: I 'm sorry ,
C: all that stuff .
B: was his point eight percent , er , a a result on testing on Macrophone or or training ?
C: It was training on Macrophone and testing yeah , on on meeting digits .
B: Oh .
B: So that was done already .
B: So we were B: Uh , and it 's point eight ?
B: OK .
C: Mm - hmm .
B: OK .
C: Yeah .
C: I I 've just been text <comment> testing the new Aurora front - end with well , Aurora system actually C: so front - end and HTK , um , acoustic models on the meeting digits
C: and it 's a little bit better than the previous system .
C: We have I have two point seven percent error rate .
C: And before with the system that was proposed , it 's what ? It was three point nine .
C: So .
B: Oh , that 's a lot better .
C: We are getting better .
B: So , what w ?
G: With the with the HTK back - end ? What we have for Aurora ?
C: And C: Yeah .
C: Two point seven .
G: I know in the meeting , like C: On the meeting we have two point seven .
G: Right .
G: Oh .
F: That 's with the new IIR filters ?
C: Uh . Yeah , yeah .
F: OK .
C: So , yeah ,
C: we have the new LDA filters ,
C: and C: I think , maybe I didn't look , but one thing that makes a difference is this DC offset compensation .
C: Uh , eh Do y did you have a look at at the meet uh , meeting digits , if they have a DC component ,
C: or ?
E: I E: I didn't .
E: No .
C: Oh .
B: Hmm .
G: No . The DC component could be negligible .
G: I mean , if you are recording it through a mike .
G: I mean , any all of the mikes have the DC removal some capacitor sitting right in that bias it .
B: Yeah .
B: But this B: uh , uh , uh , no .
B: Because , uh , there 's a sample and hold in the A - toD.
B: And these period these typically do have a DC offset .
G: Oh , OK .
B: And and they can be surprisingly large .
B: It depends on the electronics .
G: Oh ,
G: so it is the digital G: OK .
G: It 's the A - toD that introduces the DC in .
B: Yeah . The microphone isn't gonna pass any DC .
G: Yeah . Yeah . Yeah .
B: But but ,
G: OK .
B: typi
B: you know , unless B: Actually , there are instrumentation mikes that that do pass go down to DC .