PhD A: It Professor D: right . Right , it doesn't work . So , in a way , that 's , you know , that 's sort of the dominant thing is that even , say on the development set stuff that we saw , the , uh , the numbers that , uh , that Alcatel was getting when choosing out the best single numbers , it was just you know , it wasn't good enough for for a a for a real system .
PhD A: Mmm . Mm - hmm .
Professor D: You you you , um So , uh , we still have stuff to do .
PhD A: Yeah .
Professor D: Uh , and , uh I don't know So , looking at the data , where , you know what 's the what 's what 's th what 's characteristic i e yeah , I think that 's that 's a good thing . Does a any you have any thoughts about what else y you 're thinking that you didn't get to that you would like to do if you had more time ? Uh
PhD E: Oh , f a lot of thing . Because we trying a lot of s thing , and we doesn't work , we remove these . Maybe we trying again with the articulatory feature . I don't know exactly because we tried we some one experiment that doesn't work . Um , forgot it , something I don't know exactly
Professor D: Mm - hmm .
PhD E: because , tsk {comment} maybe do better some step the general , eh , diagram .
Professor D: Mm - hmm .
PhD E: I don't know exactly s to think what we can improve .
Professor D: Yeah , cuz a lot of time it 's true , there were a lot of times when we 've tried something and it didn't work right away , even though we had an intuition that there should be something there . And so then we would just stop it . Um And , uh , one of the things I don't remember the details on , but I remember at some point , when you were working with a second stream , and you tried a low - pass filtering to cepstrum , in some case you got PhD E: MSG Yeah .
Professor D: Well , but it was {comment} an MSG - like thing , but it wasn't MSG , right ? Uh , you y I think in some case you got some little improvement , but it was , you know , sort of a small improvement , and it was a a big added complication , so you dropped it . But , um , that was just sort of one try , right ? You just took one filter , threw it there ,
PhD A: Yeah ,
Professor D: right ? And it seems to me that , um , if that is an important idea , which , you know , might be , that one could work at it for a while , as you 're saying .
PhD A: Hmm .
Professor D: And , uh Uh , and you had , you know , you had the multi - band things also , and , you know , there was issue of that .
PhD A: Yeah ,
Professor D: Um , Barry 's going to be , uh , continuing working on multi - band things as well .
PhD E: Mm - hmm .
Professor D: We were just talking about , um , some , uh , some work that we 're interested in . Kind of inspired by the stuff by Larry Saul with the , uh uh , learning articulatory feature in I think , in the case of his paper with sonorance based on , uh , multi - band information where you have a a combination of gradient learning an and , uh , EM .
PhD A: Mm - hmm .
Professor D: Um , and   Um , so , I think that , you know , this is a , uh this is a neat data set . Um , and then , uh , as we mentioned before , we also have the the new , uh , digit set coming up from recordings in this room . So , there 's a lot of things to work with . Um and , uh what I like about it , in a way , is that , uh , the results are still so terrible . Uh  Uh  I mean , they 're much better than they were , you know . We 're talking about thirty to sixty percent , uh , error rate reduction . That 's that 's really great stuff to to do that in relatively short time . But even after that it 's still , you know , so poor that that , uh , no one could really use it . So , um I think that 's great that because and y also because again , it 's not something sometimes we 've gotten terrible results by taking some data , and artificially , you know , convolving it with some room response , or something we take a very Uh , at one point , uh , Brian and I went downstairs into the the basement where it was it was in a hallway where it was very reverberant and we we made some recordings there . And then we  we , uh uh , made a simulation of the of the room acoustics there and and applied it to other things ,
PhD A: Mm - hmm .
Professor D: and uh But it was all pretty artificial , and and , you know , how often would you really try to have your most crucial conversations in this very reverberant hallway ? Um So , uh This is what 's nice about the Aurora data and the data here , is that is that it 's sort of a realistic room situation uh , acoustics acoustic situation , both terms in noise and reflections , and so on and n n And , uh , uh , with something that 's still relatively realistic , it 's still very very hard to do very well . So Yeah .
PhD A: Yeah , so d well Actually , this is tha that 's why we well , it 's a different kind of data . We 're not we 're not used to work with this kind of data . That 's why we should have a loo more closer look at what 's going on .
Professor D: Yeah .
PhD E: Mm - hmm .
PhD A: Um Yeah . So this would be the first thing , and then , of course , try to well , kind of debug what was wrong , eh , when we do Aurora test on the MSG particularly , and on the multi - band .
Professor D: Yeah . Yeah . Yeah .
PhD A: Uh
Professor D: Yeah . Yeah . No , I I think there 's lots of lots of good things to do with this . So Um So let 's I guess You were gonna say something else ? Oh , OK . What do you think ?
PhD C: About
Professor D: Anything
PhD C: About other experiments ? Uh , now , I 'm interested in , um , uh looking at the experiments where you use , um uh , data from multiple languages to train the neural net . And I don't know how far , or if you guys even had a chance to try that , but that would be some it 'd be interesting to me .
PhD A: Yeah , but
Professor D: S b
PhD A: Again , it 's the kind of of thing that , uh , we were thin thinking thinking that it would work , but it didn't work . And , eh , so there is kind of of not a bug , but something wrong in what we are doing , perhaps .
Professor D: Yeah .
PhD C: Right . Right .
PhD A: Uh , something wrong , perhaps in the just in the the fact that the labels are PhD C: Right .
PhD A: well
PhD C: Mm - hmm .
PhD A: What worked best is the hand - labeled data .
PhD C: Mm - hmm .
PhD A: Um Uh , so , yeah . I don't know if we can get some hand - labeled data from other languages .
PhD C: Yeah .
PhD A: It 's not so easy to find .
PhD C: Right .
PhD A: But that would be something interesting t to to see .
PhD C: Yeah , yeah .
Professor D: Yeah . Also , uh , I mean , there was just the whole notion of having multiple nets that were trained on different data . So one form of different data was is from different languages , but the other Well , i in fact , uh , m in those experiments it wasn't so much combining multiple nets , it was a single net that had different
PhD A: Yeah .
Professor D: So , first thing is would it be better if they were multiple nets , for some reason ? Second thing is , never mind the different languages , just having acoustic conditions rather than training them all up in one , would it be helpful to have different ones ? So , um That was a question that was kind of raised by Mike Shire 's thesis , and on in that case in terms of reverberation . Right ? That that sometimes it might be better to do that . But , um , I don't think we know for sure . So , um Right . So , next week , we , uh , won't meet because you 'll be in Europe . Whe - when are you two getting back ?
PhD E: Um , I 'm
PhD A: You on Friday or S on Saturday or ?
PhD E: Sunday
PhD A: S oh yeah , Sunday , yeah .
PhD E: because it 's it 's less expensive , the price the price the ticket .
PhD C :
Professor D: Yeah , that 's right . You 've gotta S have a Saturday overnight , right ?
PhD A: I 'll be back on Tuesday .
Professor D: Tuesday .
PhD C: Where where 's the meeting ?
Professor D: Uh , Amsterdam , I think , yeah ?
PhD A: Yeah , Amsterdam .
PhD C: Uh - huh .
Professor D: Yeah . Yeah , yeah . Yep . Um So , we 'll skip next week , and we 'll meet two weeks from now . And , uh , I guess the main topic will be , uh , you telling us what happened .
PhD A: Yeah .
PhD E: Yeah .
Professor D: Uh , so Yeah , well , if we don't have an anything else to discuss , we should , uh , turn off the machine and then say the real nasty things .
PhD C: Should we do digits first ?
PhD A: Yeah .
Grad B: Oh , yeah , digits .
Professor D: Oh yeah , digits ! Yeah . Good point . Yeah , good thinking . Why don't you go ahead .
PhD C: OK . OK .

Summary: The team gathered some ideas on how they could further improve the model, perhaps by focusing more so on MSG and multi-band. The professor iterated that the task was pretty hard, and their model was still not good enough for a real system. The team was generally excited about the Aurora data, as it was more realistic, and about further pursuing the task.