Professor B: So I wonder , um , Well , what 's your what 's your thought about what to do next with it ?
PhD C: Um , I don't know . I 'm surprised , because I expected the neural net to help more when there is more mismatch , as it was the case for the Professor B: Mm - hmm .
PhD D: So , was the training set same as the p the February proposal ? OK .
PhD C: Yeah , it 's the same training set , so it 's TIMIT with the TI - digits ' , uh , noises , uh , added .
PhD D :
Professor B: Mm - hmm .
PhD C: Um Professor B: Well , we might uh , we might have to experiment with , uh better training sets . Again . But ,
PhD C: Mm - hmm .
Professor B: I The other thing is , I mean , before you found that was the best configuration , but you might have to retest those things now that we have different The rest of it is different , right ? So , um , uh , For instance , what 's the effect of just putting the neural net on without the o other other path ?
PhD C: Mm - hmm .
Professor B: I mean , you know what the straight features do .
PhD C: Yeah .
Professor B: That gives you this . You know what it does in combination .
PhD C: Mm - hmm .
Professor B: You don't necessarily know what PhD A: What if you did the Would it make sense to do the KLT on the full set of combined features ? Instead of just on the PhD C: Yeah . I g I guess . Um . The reason I did it this ways is that in February , it we we tested different things like that , so , having two KLT , having just a KLT for a network , or having a global KLT .
PhD A: Oh , I see .
PhD C: And PhD A: So you tried the global KLT before
PhD C: Well PhD A: and it didn't really PhD C: Yeah . And , uh , th Yeah .
PhD A: I see .
PhD C: The differences between these configurations were not huge , but it was marginally better with this configuration .
PhD A: Uh - huh . Uh - huh .
Professor B: But , yeah , that 's obviously another thing to try ,
PhD C: Um .
Professor B: since things are things are different .
PhD C: Mm - hmm . Mm - hmm .
Professor B: And I guess if the These are all so all of these seventy - three features are going into , um , the , uh the HMM .
PhD C: Yeah .
Professor B: And is are i i are are any deltas being computed of tha of them ?
PhD C: Of the straight features , yeah .
Professor B: n Not of the PhD C: So . But n th the , um , tandem features are u used as they are .
Professor B: Are not .
PhD C: So , yeah , maybe we can add some context from these features also as Dan did in in his last work .
Professor B: Could . i Yeah , but the other thing I was thinking was , um Uh , now I lost track of what I was thinking . But .
PhD A: What is the You said there was a limit of sixty features or something ?
PhD C: Mm - hmm .
PhD A: What 's the relation between that limit and the , um , forty - eight uh , forty eight hundred bits per second ?
Professor B: Oh , I know what I was gonna say .
PhD C: Um , not no relation .
Professor B: No relation .
PhD A: So I I I don't understand ,
PhD C: The f the forty - eight hundred bits is for transmission of some features .
PhD A: because i I mean , if you 're only using h
PhD C: And generally , i it s allows you to transmit like , fifteen , uh , cepstrum .
Professor B: The issue was that , um , this is supposed to be a standard that 's then gonna be fed to somebody 's recognizer somewhere which might be , you know , it it might be a concern how many parameters are use u used and so forth . And so , uh , they felt they wanted to set a limit . So they chose sixty . Some people wanted to use hundreds of parameters and and that bothered some other people .
PhD A: Uh - huh .
Professor B: u And so they just chose that . I I I think it 's kind of r arbitrary too . But but that 's that 's kind of what was chosen . I I remembered what I was going to say . What I was going to say is that , um , maybe  maybe with the noise removal , uh , these things are now more correlated . So you have two sets of things that are kind of uncorrelated , uh , within themselves , but they 're pretty correlated with one another .
PhD C: Mm - hmm .
Professor B: And , um , they 're being fed into these , uh , variants , only Gaussians and so forth , and and , uh ,
PhD C: Mm - hmm .
Professor B: so maybe it would be a better idea now than it was before to , uh , have , uh , one KLT over everything , to de - correlate it .
PhD C: Mm - hmm . Yeah , I see .
Professor B: Maybe . You know .
PhD D: What are the S N Rs in the training set , TIMIT ?
PhD C: It 's , uh , ranging from zero to clean ? Yeah . From zero to clean .
PhD D: Mm - hmm .
Professor B: Yeah . So we found this this , uh this Macrophone data , and so forth , that we were using for these other experiments , to be pretty good .
PhD C: Mm - hmm .
Professor B: So that 's i after you explore these other alternatives , that might be another way to start looking , is is just improving the training set .
PhD C: Mm - hmm .
Professor B: I mean , we were getting , uh , lots better recognition using that , than Of course , you do have the problem that , um , u i {comment} we are not able to increase the number of Gaussians , uh , or anything to , uh , uh , to match anything . So we 're only improving the training of our feature set , but that 's still probably something .
PhD A: So you 're saying , add the Macrophone data to the training of the neural net ? The tandem net ?
Professor B: Yeah , that 's the only place that we can train .
PhD A: Yeah .
Professor B: We can't train the other stuff with anything other than the standard amount ,
PhD A: Right .
Professor B: so . Um , um PhD A: What what was it trained on again ? The one that you used ?
PhD C: It 's TIMIT with noise .
PhD A: Uh - huh .
Professor B: Yeah .
PhD C: So , yeah , it 's rather a small Professor B: How big is the net , by the way ?
PhD C: Um , Uh , it 's , uh , five hundred hidden units . And Professor B: And again , you did experiments back then where you made it bigger and it and that was that was sort of the threshold point . Much less than that , it was worse ,
PhD C: Yeah .
Professor B: and
PhD C: Yeah .
Professor B: much more than that , it wasn't much better . Hmm .
PhD C: Yeah . @ @ ?
PhD D: So is it is it though the performance , big relation in the high ma high mismatch has something to do with the , uh , cleaning up that you that is done on the TIMIT after adding noise ?
PhD C :
PhD D: So it 's i All the noises are from the TI - digits ,
PhD C: Yeah .
PhD D: right ? So you i
PhD C: Um They k uh PhD D: Well , it it 's like the high mismatch of the SpeechDat - Car after cleaning up , maybe having more noise than the the training set of TIMIT after clean s after you do the noise clean - up .
PhD C: Mmm .
PhD D: I mean , earlier you never had any compensation , you just trained it straight away .
PhD C: Mm - hmm .
PhD D: So it had like all these different conditions of S N Rs , actually in their training set of neural net .
PhD C: Mm - hmm . Mm - hmm .
PhD D: But after cleaning up you have now a different set of S N Rs , right ?
PhD C: Yeah .
PhD D: For the training of the neural net .
PhD C: Mm - hmm .
PhD D: And is it something to do with the mismatch that that 's created after the cleaning up , like the high mismatch PhD C: You mean the the most noisy occurrences on SpeechDat - Car might be a lot more noisy than PhD D: Mm - hmm . Of that I mean , the SNR after the noise compensation of the SpeechDat - Car .
Professor B: Oh , so Right . So the training the the neural net is being trained with noise compensated stuff .
PhD C: Maybe .
PhD D: Yeah .
PhD C: Yeah , yeah .
Professor B: Which makes sense ,
PhD D: Yeah .
Professor B: but , uh , you 're saying Yeah , the noisier ones are still going to be , even after our noise compensation , are still gonna be pretty noisy .
PhD D: Yeah .
PhD C: Mm - hmm .
PhD D: Yeah , so now the after - noise compensation the neural net is seeing a different set of S N Rs than that was originally there in the training set . Of TIMIT . Because in the TIMIT it was zero to some clean .
Professor B: Right . Yes .
PhD D: So the net saw all the SNR @ @ conditions .
Professor B: Right .
PhD D: Now after cleaning up it 's a different set of SNR .
Professor B: Right .
PhD D: And that SNR may not be , like , com covering the whole set of S N Rs that you 're getting in the SpeechDat - Car .
Professor B: Right , but the SpeechDat - Car data that you 're seeing is also reduced in noise by the noise compensation .
PhD C: Yeah .
PhD D: Yeah , yeah , yeah , yeah , it is . But , I 'm saying , there could be some some issues of Professor B: So .
PhD C: Mm - hmm .
Professor B: Yeah .
PhD C: Well , if the initial range of SNR is different , we the problem was already there before . And Professor B: Yeah .
PhD C: Because Mmm Professor B: Yeah , I mean , it depends on whether you believe that the noise compensation is equally reducing the noise on the test set and the training set .
PhD C: Hmm .
Professor B: Uh PhD D: On the test set , yeah .
Professor B: Right ? I mean , you 're saying there 's a mismatch in noise that wasn't there before ,
PhD D: Hmm . Mm - hmm .
Professor B: but if they were both the same before , then if they were both reduic reduced equally , then , there would not be a mismatch .
PhD D: Mm - hmm .
Professor B: So , I mean , this may be Heaven forbid , this noise compensation process may be imperfect , but . Uh , so maybe it 's treating some things differently .
PhD C: Yeah , uh PhD D: Well , I I don't know . I I just that could be seen from the TI - digits , uh , testing condition because , um , the noises are from the TI - digits , right ? Noise PhD C: Yeah . So PhD D: So cleaning up the TI - digits and if the performance goes down in the TI - digits mismatch high mismatch like this PhD C: Clean training , yeah .
PhD D: on a clean training , or zero DB testing .
PhD C: Yeah , we 'll so we 'll see . Uh .
PhD D: Yeah .
PhD C: Maybe .
PhD D: Then it 's something to do .
PhD C: Mm - hmm .
Professor B: I mean , one of the things about PhD C: Yeah .
Professor B: I mean , the Macrophone data , um , I think , you know , it was recorded over many different telephones .
PhD C: Mm - hmm .
Professor B: And , um , so , there 's lots of different kinds of acoustic conditions . I mean , it 's not artificially added noise or anything . So it 's not the same . I don't think there 's anybody recording over a car from a car , but I think it 's it 's varied enough that if if doing this adjustments , uh , and playing around with it doesn't , uh , make it better , the most uh , it seems like the most obvious thing to do is to improve the training set . Um I mean , what we were uh the condition It it gave us an enormous amount of improvement in what we were doing with Meeting Recorder digits , even though there , again , these m Macrophone digits were very , very different from , uh , what we were going on here . I mean , we weren't talking over a telephone here . But it was just I think just having a a nice variation in acoustic conditions was just a good thing .
PhD C: Mm - hmm . Yep .
PhD D: Mmm .