Working on the second stream Of course we have ideas on this also but w we need to try different things and Uh but their noise estimation um uh I mean back on the second stream I mean something talked about for a while I mean I think certainly a high hope Yeah Mmm Um so we have this this default idea about just using some sort of purely spectral thing Uh yeah for But second stream um we we did a first try with this and it it clearly hurts But uh Uh how was the stream combined It was c it was just combined um by the acoustic model So there was no neural network for Right moment mean if you just had a second stream that was just spectral and had another neural net Yeah and combined there that that uh might be good Mmm Yeah Um Yeah and the other thing that noise estimation and th um maybe try to train uh the training data for the t tandem network right now is like i is using the noises from the Aurora task and I think that people might um try to argue about that because then in some cases we have the same noises in for training the network than Right the noises that are used for testing and So we have t n uh to try to get rid of these Yeah this Maybe problem put in some other noise something Yeah different I mean it probably helpful to have have a little noise there But it may be something else th at least Yeah you could say it was And then if it doesn't hurt too much though Yeah a good idea Um Yeah The last thing is that I think we are getting close to human performance Well something I would like to investigate further but um I did like um I did uh listen to the m most noisy utterances of the Italian and tried to transcribe them And um is a particular human This is this i Yeah this So Stephane Stephane the the flaw of the experiment This is just i j Yeah Getting close just one subject but but still uh what happens is is that uh the digit error rate on this is around one percent Yeah while our system is currently at seven percent Um but what happens also is that if I listen to the um a version of the speech and I this using a white noise filtered by a LPC Yeah uh filter Um well you can argue that uh that this Yeah speech so the ear is not trained to recognize this But s actually it sound like whispering so we are Well eh two problems there I mean I mean so so the first is that by doing with synthesized speech w like saying uh i i adding other degradation Right So not just the noise but adding in fact some degradation because only an approximation Um and the second thing is which is m maybe more interesting is that um if you do it with whispered speech you get this number What if you had done analysis and taken the pitch as well Alright So now you put the pitch in What would the percentage be then Um See the question So you see if if if uh say back down to one percent again That would say at least for people having the pitch is really really important which would be interesting in itself Uh Um But if i on the other hand if it stayed up near five percent then say boy LPC n twelve is pretty crummy You know So I I not sure not sure how we can conclude from this anything about that our Ye system is close to Yeah Well the point human is performance ey the point is that um what I what I listened to when I the LP the spectrum is in a way what the system uh is hearing cuz all the all the um excitation all the well the excitation is is not taken into account what we do with our system And Well this not case doing the LPC I mean so so what Well you did LPC sure but What if you did LPC Twenty Right I mean th the thing is LPC is not a a really great representation of speech So all saying is that you have in addition to the w the uh removal of pitch you also are doing uh a particular parameterization which um uh Mmm Uh so see how would you do So fo what we do with our systems And No Actually we d we we don't because we do we do uh uh mel filter bank for instance but Right is it that is it that different Um I don't know I mel uh based synthesis would sound like but certainly the spectra are quite different Couldn't you t couldn't you um test the human performance on just the original audio This is the one percent number Yeah one percent trying to remove the pitch information Oh oh and OK closer see to what to what seeing as the feature vectors OK So y uh your performance was one percent and then when you with it went to five Yeah OK I mean We were we were j It it a little bit still apples and oranges because we are choosing these features in order to be the best for recognition And um i if you listen to them they still might not be very Even if you made something closer to what gonna i it might not sound very good Yeah Uh and i the degradation from that might might actually make it even harder uh to understand than the So all saying is that the puts in synthesis puts in some degradation not what used to hearing and is um not not just a question of how much information is there as if you will always take maximum advantage of any information presented to you In fact you hear some things better than others And so it it But isn't But I agree that it says that uh the kind of information that feeding it is probably um um a little bit um minimal definitely some things that thrown away And why I was saying it might be interesting if you an interesting test of this would be if you if you actually put the pitch back in So you just extract it from the actual speech and put it back in and see does that is that does that make the difference If that if that takes it down to one percent again then say OK in fact having um not just the spectral envelope but also the also the the pitch that uh Mmm information that people can use anyway But from this pretty safe to say that the system is with either two to seven percent away from the performance of a human Right So Well somewhere or in that range Yeah so