B: But I guess it 's still within their rules to have have it on the , uh , t uh , server side .
D: Yeah . Yeah .
B: Right ?
B: OK .
D: Mmm .
B: And this is still ?
B: Uh , well , y you 're saying here .
B: I c I should just let you go on .
D: Yeah , there were small tricks to make this tandem network work .
D: Uh , mmm ,
D: and one of the trick was to , um , use some kind of hierarchical structure
D: where the silence probability is not computed by the final tandem network but by the VAD network .
D: Um ,
D: so apparently it looks better when , uh , we use the silence probability from the VAD network
D: and we re - scale the other probabilities by one minus the silence probability .
B: Huh .
D: Um .
D: So it 's some kind of hierarchical thing , uh , that Sunil also tried , um ,  on SPINE
D: and apparently it helps a little bit also .
D: Mmm .
D: And .
D: Yeah , the reason w why why we did that with the silence probability was that , um B: Could ? Uh , uh ,
B: I 'm I 'm really sorry .
B: Can you repeat what you were saying about the silence probability ?
D: Mm - hmm .
B: I only D: Yeah .
B: My mind was some D: So there is the tandem network that e e e estimates the phone probabilities
B: Yeah .
B: Yeah .
D: and the silence probabilities also .
B: Right .
D: And things get better when , instead of using the silence probability computed by the tandem network , we use the silence probability , uh , given by the VAD network ,
B: Oh .
D: um ,
B: The VAD network is ?
D: Which is smaller ,
D: but maybe ,
D: um D: So we have a network for the VAD which has one hundred hidden units ,
D: and the tandem network has five hundred .
D: Um .
D: So it 's smaller
D: but th the silence probability from this network seems , uh , better .
B: OK .
D: Mmm .
D: Uh .
D: Well , it looks strange ,
D: but B: Yeah . But D: but it
B: OK .
D: Maybe it 's has something to do to the fact that we don't have infinite training data
D: and B: We don't ?
D: Well !
D: And
D: so Well , things are not optimal
D: and B: Yeah .
D: Mmm E: Are you you were going to say why what made you wh what led you to do that .
D: Yeah . Uh , there was a p <comment> problem that we observed , um ,  that there was there were , like , many insertions in the in the system .
B: Mm - hmm .
D: Mmm .
B: Hmm .
D: Actually plugging in the tandem network was increasing , I I I think , the number of insertions .
B: Mm - hmm .
D: And , um D: So it looked strange
D: and then just using the the other silence probability helps .
D: Mmm .
D: Um D: Yeah . The next thing we will do is train this tandem on more data .
D: Um B: So , you know , in a way what it might i it 's it 's a little bit like combining knowledge sources .
B: Right ?
B: Because the fact that you have these two nets that are different sizes means they behave a little differently ,
D: Mm - hmm .
B: they find different things .
B: And , um ,
B: if you have , um f the distribution that you have from , uh , f speech sounds is w <comment> sort of one source of knowledge .
D: Mm - hmm .
B: And this is B: and rather than just taking one minus that to get the other ,
B: which is essentially what 's happening ,
B: you have this other source of knowledge that you 're putting in there .
B: So you make use of both of them in in what you 're ending up with .
B: Maybe it 's better .
D: Yeah .
B: Anyway , you can probably justify anything if what 's use
D: Yeah .
D: And and the features are different also .
B: Yeah .
D: I mean , the VAD doesn't use the same features there are .
B: Mm - hmm .
E: Hmm .
B: Oh !
D: Um B: That might be the key , actually .
D: Mm - hmm .
B: Cuz you were really thinking about speech versus nonspeech for that .
D: Mm - hmm .
B: That 's a good point .
D: Mmm .
D: Uh . Well , there are other things that we should do
D: but , um , it requires time
D: and  We have ideas ,
D: like so , these things are like hav having a better VAD .
D: Uh , we have some ideas about that .
D: It would  probably implies working a little bit on features that are more suited to a voice activity detection .
B: Mm - hmm .
D: Working on the second stream .
D: Of course we have ideas on this also ,
D: but  w we need to try different things
D: and D: Uh , but their noise estimation ,
D: um  uh B: I mean , back on the second stream ,
B: I mean , that 's something we 've talked about for a while .
B: I mean , I think <nonvocalsound> that 's certainly a high hope .
D: Yeah . Mmm .
B: Um ,
B: so we have this this default idea about just using some sort of purely spectral thing ?
D: Uh , yeah .
B: for a second stream ?
D: But , um , we we did a first try with this ,
D: and it it clearly hurts .
B: But , uh , how was the stream combined ?
D: Uh . It was c it was just combined , um , by the acoustic model .
D: So there was , no neural network for the moment .
B: Right . So , I mean , if you just had a second stream that was just spectral and had another neural net and combined there , that that , uh , might be good .
D: Mm - hmm .
D: Yeah . Mm - hmm .
D: Mm - hmm .
D: Mmm .
D: Yeah .
D: Um D: Yeah , and the other thing , that noise estimation
D: and th um , maybe try to train uh , the training data for the t
D: tandem network , right now , is like i is using the noises from the Aurora task
D: and  I think that people might , um , try to argue about that
D: because then in some cases we have the same noises in for training the network than the noises that are used for testing ,
B: Right .
D: and D: So we have t n uh , to try to get rid of these  this problem .
B: Yeah . Maybe you just put in some other noise ,
D: Mm - hmm . Yeah .
B: something that 's different .
B: I mean , it it 's probably helpful to have have a little noise there .
D: Uh - huh .
B: But it may be something else
B: th at least you could say it was .
D: Yeah .
B: And then if it doesn't hurt too much , though .
D: Uh - huh .
B: Yeah . That 's a good idea .
D: Um .
D: Yeah . The last thing is that I think we are getting close to human performance .
D: Well , that 's something I would like to investigate further ,
D: but ,
D: um ,
D: I did , like , um I did , uh , listen to the m most noisy utterances of the SpeechDat - Car Italian
D: and tried to transcribe them .
D: And , um B: So this is a particular human .
B: This is this i this is Stephane .
D: Yeah . So that 's that 's E: St - Stephane .
B: Yeah .
D: that 's the the flaw of the experiment .
D: This is just i j <comment>  it 's just one subject ,
B: Yeah .
E: Getting close .
D: but D: but still , uh , what happens is is that , uh , the digit error rate on this is around one percent ,
B: Yeah .
D: while our system is currently at seven percent .
D: Um , but what happens also is that if I listen to the , um <nonvocalsound> a re - synthesized version of the speech
D: and I re - synthesized this using a white noise that 's filtered by a LPC , uh , filter B: Yeah .
D: Um ,
D: well , you can argue , that , uh that this is not speech ,
B: Yeah .
D: so the ear is not trained to recognize this .
D: But s actually it sound like whispering ,
D: so we are B: Well , I mean , it 's D: eh B: There 's two problems there .
B: I mean I mean , so so the first is that by doing LPC - twelve with synthesized speech w like you 're saying , uh , it 's  i i you 're you 're adding other degradation .
D: Uh - huh .
B: Right ?
B: So it 's not just the noise
B: but you 're adding in fact some degradation
B: because it 's only an approximation .
B: Um ,
B: and the second thing is which is m maybe more interesting is that , um , <comment> if you do it with whispered speech , you get this number .
B: What if you had done analysis <comment> re - synthesis and taken the pitch as well ?
B: Alright ?
B: So now you put the pitch in .
D: Uh - huh .
B: What would the percentage be then ?
D: Um B: See , that 's the question .
B: So , you see , if it 's if it 's if it 's , uh Let 's say it 's back down to one percent again .