PhD G: So that 's actually good . Ma - maybe . I mean , the tele we had telephone bandwidth for Switchboard and we had the an annoying sort of telephone handset movement problem that I think may also affect it .
PhD D: Hmm .
PhD G: So we 're just getting better signals in in this data . Which is nice . So .
Professor F: Yeah .
PhD G: Anyway , Don 's been doing a great job and we hope to continue with , um , Andreas 's help and also some of Thilo 's help on this ,
Professor F: Great .
PhD E: Y
PhD G: to to try to get a non - cheating version of how all this would work .
PhD E: Yeah . Sure . Yeah .
Professor F: Has has , uh ? We just I think , just talked about this the other day , but h has has anybody had a chance to try changing , uh , insertion penalty sort of things with the with the , uh  uh , using the tandem system input for the ?
PhD C: Oh , yeah . I tried that . It didn't , um , help dramatically . The PhD D: Were they out of balance ? I didn't I didn't notice .
PhD C: There were a little the relative number of I think there were a higher number of deletions , actually .
Professor F: Oh .
PhD C: So , you , uh So , actually it it preferred to have a positive er , negative insertion penalty ,
PhD G: Deletions ?
PhD C: which means that , um Professor F: Uh - huh .
PhD C: But , you know , it didn't change th the by adjusting that the , um Professor F: OK .
PhD C: Yeah . The error changed by probably one percent or so . But , you know , given that that word error rate is so high , that 's not a Professor F: OK . So that So that 's So that 's not the problem .
PhD C: That 's not the problem . No .
Professor F: Yeah .
PhD C: But , uh , we s just , um , uh you know , Chuck and I talked and the @ @ {comment} next thing to do is probably to tune the um , the size of the Gaussian system , um , @ @ {comment} to to this to this feature vector , which we haven't done at all . We just used the same configuration as we used for the  for the standard system .
Professor F: Hmm .
PhD C: And , for instance , uh , Dan @ @ {comment} Dan just sent me a message saying that CMU used , um , something like ten Gaussians per cluster You know , each each mixture has ten Gaussians
PhD D: Mm - hmm . Hmm . We 're using sixty - four ,
PhD C: and and we 're using sixty - four ,
PhD D: right ?
PhD C: so that 's obviously a big difference
PhD D: Yeah .
PhD C: and it might be way off and give very poorly trained , uh , you know , Gaussians that way ,
Professor F: Hmm .
PhD C: uh , an and poorly trained mixture weights . So so , we have The turn - around time on the training when we train only the a male system with , uh , you know , our small training set , is less than twenty - four hours , so we can run lots of  uh , basically just brute force , try a whole bunch of different um , settings .
Professor F: OK .
PhD C: And , uh , with the new machines it 'll be even better . So .
Professor F: Yeah . We get twelve of those ,
PhD C: Yeah .
Professor F: huh ?
PhD C: But the PLP features work um , uh , you know , continue to improve the ,
Professor F: OK .
PhD C: um As I said before , the uh using Dan 's , uh , uh , vocal tract normalization option works very well . So , um , @ @ {comment} I ran one experiment where we 're just did the vocal tract le normalization only in the test data ,
Professor F: Mm - hmm .
PhD C: so I didn't bother to retrain the models at all , and it improved by one percent , which is about what we get with  uh , with , you know , just @ @ {comment} actually doing both training and test normalization , um , with , um , the , uh  uh , with the standard system . So , in a few hours we 'll have the numbers for the for retraining everything with vocal tract length normalization and So , that might even improve it further .
Professor F: Great .
PhD C: So , it looks like the P L - fea P features {comment} do very well now with after having figured out all these little tricks to to get it to work .
Professor F: Yeah .
PhD C: So .
Professor F: Good .
PhD G: Wait . So you mean you improve one percent over a system that doesn't have any V T L in it already ?
PhD C: Exactly . Yeah .
PhD G: OK .
Professor F: Yeah . OK . So then then we 'll have our baseline to to compare the currently hideous , uh , uh , new thing with .
PhD C: Right . a Right . And and what that suggests also is of course that the current Switchboard MLP isn't trained on very good features .
Professor F: But Yeah .
PhD C: Uh , because it was trained on whatever , you know , was used , uh , last time you did Hub - five stuff , which didn't have any of the Professor F: Right . But all of these effects were j like a couple percent .
PhD C: Uh .
Professor F: Right ? I mean , y the PhD C: Well , but if you add them all up you have , uh , almost five percent difference now .
Professor F: Add all of them . I thought one was one point five percent and one was point eight .
PhD C: Yeah . And now we have another percent with the V T
Professor F: That 's three point three .
PhD C: Um , actually , and it 's ,  um , What 's actually qu interesting is that with um , well , you m prob maybe another half percent if you do the VTL in training , and then interestingly , if you optimize you get more of a win out of rescoring the , um , uh , the N best lists , uh , and optimizing the weights , um , uh than PhD D: Than you do with the standard ?
PhD C: Yeah . So Professor F: Yeah . But the part that 's actually adjustment of the front - end per se as opposed to doing putting VTLN in or something is it was a couple percent .
PhD C: Right .
Professor F: Right ? It was it was there was  there was one thing that was one and a half percent and one that was point eight . So and and let me see if I remember what they were . One of them was , uh , the change to , uh because it did it all at once , {comment} to uh , from bark scale to mel scale ,
PhD C: Mm - hmm .
Professor F: which I really feel like saying in quotes , because @ @ {comment} they 're essentially the same scale but the but but but any i individual particular implementation of those things puts things in a particular place .
PhD G: Yeah . Why did that cha ?
PhD C: Mm - hmm .
Professor F: So that 's why I wanted to look I still haven't looked at it yet . I I wanna look at exactly where the filters were in the two ,
PhD C: Mm - hmm .
Professor F: and it  it 's probably something like there 's one fewer or one more filter in the sub one kilohertz band
PhD C: Mm - hmm .
Professor F: and for whatever reason with this particular experiment it was better one way or the other .
PhD G: Hmm .
Professor F: Um , it could be there 's something more fundamental but it you know , I I don't know it yet . And the other and the other that was like one and a half or something , and then there was point eight percent , which was what was the other thing ?
PhD D: Well , that was combined with the triangular . Right ?
Professor F: Yeah . Those those two were together .
PhD D: Yeah . Right .
Professor F: We d weren't able to separate them out cuz it was just done in one thing . But then there was a point eight percent which was something else .
PhD D: The low - frequency cut - off .
Professor F: Do you remember the ? Oh , yeah . So that was that was , uh that one I can claim credit for , uh , i in terms of screwing it up in the first place . So that someone e until someone else fixed it , which is that , um , I never put when I u We had some problems before with offsets . This inf this went back to , uh , I think Wall Street Journal .
PhD C: Hmm .
Professor F: So we we had , uh ea everybody else who was doing Wall Street Journal knew that there were big DC offsets in th in these data in those data and and and nobody happened to mention it to us ,
PhD C: Hmm .
Professor F: and we were getting these , like , really terrible results , like two , three times the error everybody else was getting . And then in casual conversation someone ment mentioned " uh , well , I guess , you know , of course you 're taking care of the offsets . " I said " what offsets ? "
Grad B :