PhD C: So the frame - dropping is the last thing that we do . So , yeah , what we do is we compute the silence probability , convert it to that binary flag ,
Professor B: Uh - huh .
PhD C: and then in the end you c up upsample it to match the final features number of PhD E: Mm - hmm .
PhD A: Did that help then ?
PhD C: It seems to be helping on the well - matched condition . So that 's why this improvement I got from the last result . So . And it actually r reduced a little bit on the high mismatch , so in the final weightage it 's b b better because the well - matched is still weighted more than Professor B: So , @ @ I mean , you were doing a lot of changes . Did you happen to notice how much , uh , the change was due to just this frame - dropping problem ? What about this ?
PhD C: Uh , y you had something on it . Right ?
PhD E: Just the frame - dropping problem . Yeah . But it 's it 's difficult . Sometime we we change two two things together and But it 's around maybe it 's less than one percent .
Professor B: Uh - huh .
PhD C: Yeah .
PhD E: It Professor B: Well . But like we 're saying , if there 's four or five things like that then pretty sho soon you 're talking real improvement .
PhD E: Yeah . Yeah . And it Yeah . And then we have to be careful with that also with the neural net
Professor B: Yeah .
PhD E: because in {comment} the proposal the neural net was also , uh , working on after frame - dropping .
Professor B: Mm - hmm .
PhD E: Um .
Professor B: Oh , that 's a real good point .
PhD E: So . Well , we 'll have to be to do the same kind of correction .
Professor B: It might be hard if it 's at the server side . Right ?
PhD E: Mmm . Well , we can do the frame - dropping on the server side or we can just be careful at the terminal side to send a couple of more frames before and after , and So . I think it 's OK .
Professor B: OK .
PhD A: You have , um So when you Uh , maybe I don't quite understand how this works , but , um , couldn't you just send all of the frames , but mark the ones that are supposed to be dropped ? Cuz you have a bunch more bandwidth . Right ?
Professor B: Well , you could . Yeah . I mean , it it always seemed to us that it would be kind of nice to in addition to , uh , reducing insertions , actually use up less bandwidth .
PhD A: Yeah . Yeah .
Professor B: But nobody seems to have cared about that in this evaluation .
PhD A: And that way the net could use Professor B: So .
PhD A: If the net 's on the server side then it could use all of the frames .
PhD C: Yes , it could be . It 's , like , you mean you just transferred everything and then finally drop the frames after the neural net .
PhD A: Mm - hmm .
PhD C: Right ? Yeah . That 's that 's one thing which PhD E: Mm - hmm .
PhD A: But you could even mark them , before they get to the server .
PhD C: Yeah . Right now we are Uh , ri Right now what wha what we did is , like , we just mark we just have this additional bit which goes around the features , saying it 's currently a it 's a speech or a nonspeech .
PhD A: Oh , OK .
PhD C: So there is no frame - dropping till the final features , like , including the deltas are computed .
PhD A: I see .
PhD C: And after the deltas are computed , you just pick up the ones that are marked silence and then drop them .
PhD A: Mm - hmm . I see . I see .
Professor B: So it would be more or less the same thing with the neural net , I guess , actually .
PhD E: Mm - hmm .
PhD C: So . Yeah , that 's what that 's what that 's what , uh , this is doing right now .
PhD A: I see . OK .
Professor B: Yeah .
PhD E: Mm - hmm .
Professor B: Um . OK . So , uh , what 's , uh ? That 's that 's a good set of work that that , uh PhD C: Just one more thing . Like , should we do something f more for the noise estimation , because we still ?
Professor B: Yeah . I was wondering about that . That was I I had written that down there .
PhD C: Yeah .
PhD E: Mm - hmm .
Professor B: Um PhD E: So , we , uh actually I did the first experiment . This is with just fifteen frames . Um . We take the first fifteen frame of each utterance to it ,
Professor B: Yeah .
PhD E: and average their power spectra . Um . I tried just plugging the , um , uh , Guenter noise estimation on this system , and it uh , it got worse . Um , but of course I didn't play with it .
Professor B: Uh - huh .
PhD E: But Mm - hmm . Uh , I didn't do much more for noise estimation . I just tried this ,
Professor B: Hmm . Yeah . Well , it 's not surprising it 'd be worse the first time .
PhD E: and Professor B: But , um ,
PhD E: Mm - hmm .
Professor B: it does seem like , you know , i i i i some compromise between always depending on the first fifteen frames and a a always depending on a a pause is is is a good idea . Uh , maybe you have to weight the estimate from the first - teen fifteen frames more heavily than than was done in your first attempt . But PhD E: Mm - hmm .
Professor B: but PhD E: Yeah , I guess .
Professor B: Yeah . Um . No , I mean Um , do you have any way of assessing how well or how poorly the noise estimation is currently doing ?
PhD E: Mmm . No , we don't .
Professor B: Yeah .
PhD E: We don't have nothing that PhD C: Is there was there any experiment with ? Well , I I did The only experiment where I tried was I used the channel zero VAD for the noise estimation and frame - dropping . So I don't have a  I don't have a split , like which one helped more .
PhD E: Yeah .
PhD C: So . It it was the best result I could get .
PhD E: Mm - hmm .
PhD C: So , that 's the Professor B: So that 's something you could do with , um , this final system . Right ? Just do this everything that is in this final system except , uh , use the channel zero .
PhD C: Mm - hmm . For the noise estimation .
Professor B: Yeah .
PhD C: Yeah . We can try something .
Professor B: And then see how much better it gets .
PhD C: Mm - hmm . Sure .
Professor B: If it 's , you know , essentially not better , then it 's probably not worth
PhD E: Yeah .
Professor B: any more .
PhD C: Yeah . But the Guenter 's argument is slightly different . It 's , like , ev even even if I use a channel zero VAD , I 'm just averaging the  the s power spectrum . But the Guenter 's argument is , like , if it is a non - stationary segment , then he doesn't update the noise spectrum . So he 's , like he tries to capture only the stationary part in it . So the averaging is , like , different from updating the noise spectrum only during stationary segments . So , th the Guenter was arguing that , I mean , even if you have a very good VAD , averaging it , like , over the whole thing is not a good idea .
Professor B: I see .
PhD C: Because you 're averaging the stationary and the non - stationary , and finally you end up getting something which is not really the s because , you anyway , you can't remove the stationary part fr I mean , non - stationary part from the signal .
Professor B: Not using these methods anyway . Yeah .
PhD C: So Yeah . So you just update only doing or update only the stationary components . Yeah . So , that 's so that 's still a slight difference from what Guenter is trying
Professor B: Well , yeah . And and also there 's just the fact that , um , eh , uh , although we 're trying to do very well on this evaluation , um , we actually would like to have something that worked well in general . And , um , relying on having fifteen frames at the front or something is is pretty PhD C: Yeah , yeah .
Professor B: I mean , you might , you might not .
PhD C: Mmm .
PhD E: Mm - hmm .
Professor B: So , um . Um , it 'd certainly be more robust to different kinds of input if you had at least some updates . Um .