even though the software can do many things we should for now pick a set of things th these things I would guess and not change that And then focus on everything left And I think you know that our goal should be by next week when Hynek comes back uh to uh really just to have a firm path uh for the you know for the time gone of of uh what things will be attacked But I would I would I would thought think that what we would wanna do is not futz with this stuff for a while because happen is change many other things in the system and then probably wanna come back to this and possibly make some other choices But um But just conceptually where does the neural net go Do do you wanna h run it on the output of the spectrally subtracted Well depending on its size Well one question is is it on the um server side or is it on the terminal side Uh if on the server side it you probably don't have to worry too much about size So kind of an argument for that We do still however have to consider its latency So the issue is is um for instance could we have a neural net that only looked at the past Right Um what done in uh in the past is to use the neural net uh to transform um all of the features that we use So this is done early on This is essentially um um I guess more or less like a spee a speech enhancement technique here right where just kind of creating new if not new speech at least new new that that have you know which could be turned into speech uh that that have some of the noise removed Um after that we still do a mess of other things to Right to produce a bunch of features And then those features are not now currently transformed by the neural net And then the the way that we had it in our before we had the neural net transformed features and we had the untransformed features which I guess you you actually did linearly transform Yeah with Yeah KLT but but but uh to orthogonalize them but but they were not uh processed through a neural net And idea with that as I recall was that have one part of the feature vector that was very discriminant and another part that wasn't uh which would smooth things a bit for those occasions when uh the testing set was quite different than what trained your discriminant features for So um all of that is is uh still seems like a good idea The thing is now we know some other constraints We can't have unlimited amounts of latency Uh y you know still being debated by the by people in Europe but uh no matter how they end up there not going to be unlimited amounts so Yeah to be a little conscious of that Um So the neural net issue the VAD issue And uh the second stream thing And I think those that we last time we agreed that those are the three things that have to get uh focused was on issue with the VAD Well better ones are good And so the w the default uh boundaries that they provide are OK but not all that great I guess they still allow two hundred milliseconds on either side or some Is that what the deal is Uh so th um they keep two hundred milliseconds at the beginning and end of speech And they keep Outside all the the beginnings and end And all the speech pauses which is Sometimes on the you have pauses that are more than one or two seconds Wow More than one second for sure Um Hmm And yeah it seems to us that this way of just dropping the beginning and end is not We cou we can do better I think because um with this way of dropping the frames they improve over the baseline by fourteen percent and Sunil already showed that with our current VAD we can improve by more than twenty percent On top of the VAD that they provide No Just using either their VAD or Our way our current VAD Oh OK So our current VAD is is more than twenty percent while Theirs their fourteen is fourteen Yeah I see Huh So Yeah And another thing that we did also is that we have all this training data for say for We have channel zero which is clean channel one which is microphone And if we just take only the um VAD probabilities computed on the clean signal and apply them on the uh test utterances then results are much better In some cases it divides the error rate by two Wow So it means that there are stim still If if we can How good VAD how well latency great does the uh does our VAD add Is Uh significant right now um a neural net with nine frames So forty milliseconds plus um the rank ordering which uh should be Like another ten frames ten Yeah So Rank now Oh one hundred and forty With the milliseconds ordering sorry The the The the smoothing the The the the filtering of the probabilities on Yeah R not a median filtering just We don't take the median value We take something Um so we have eleven um frames Oh this is for the VAD Yeah for the VAD yeah and we Yeah take Oh th the third Yeah Dar Um Yeah Um Mmm So Yeah I was just noticing on this that it makes reference to delay So the If you ignore Um the VAD is sort of in in parallel isn't i isn't it with with the I mean it The isn't LDA additive with the the uh LDA and the Wiener filtering and Yeah forth So so what happened right now we removed the delay of the LDA Yeah So we I mean if so if we if so which is like if we reduce the delay of VA So the f the final now ba is f determined by the delay of the VAD because the LDA doesn't have any delay So if we re if we reduce the delay of the VAD I mean like effectively reducing the delay How how much uh delay was there on the LDA So the LDA and the VAD both had a hundred millisecond delay So and they were in parallel so which means you pick either one of them Mmm biggest whatever I see So right there now the LDA delays are more Oh OK And there didn't seem to be any uh penalty for that Pardon There didn't seem to be any penalty for making it causal Oh no It actually made it like point one percent better or something OK actually Or something Well like may as that well and