OK Uh Somebody else should run this sick of being the one to sort of go through and say Well what do you think about this You Should wanna take turns You want me to run it today Why don't you run it today OK OK OK Um see maybe we should just get a list of items things that we should talk about Um I guess the usual updates everybody going around and saying uh you know what working on the things that happened the last week But aside from that is there anything in particular that anybody wants to bring up Mmm for today No OK So why don't we just around and people can give updates Uh do you want to start Stephane Alright Um Well the first thing maybe is that the p Eurospeech paper is uh accepted Um Yeah This is what what do you uh in the paper there So the paper that describe basically the um system that were proposed for the one Aurora s we submitted the last round Right yeah Yeah Um Yeah So and the fff comments seems from the reviewer are good So Hmm Mmm Yeah Where it gonna be this year uh Aalborg in Denmark Oh OK And yeah September Mmm Mmm Yeah Then uh whhh well been working on on t mainly on normalization this week Uh been trying different slightly slightly different approaches Um the first thing is trying to play a little bit again with the um time constant Uh second thing is uh the training of uh normalization with two different means one mean for the silence and one for the speech Um and so I have two recursions which are controlled by the um probability of the voice activity detector Mmm This actually don't s doesn't seem to help although it doesn't hurt So But well both normalization approach seems equivalent Are the means pretty different for the two Yeah They can be very different Yeah Hmm So do you maybe make errors in different places Different kinds of errors I didn't look uh more closely Um It might be yeah Um Well eh there is one thing that we can observe is that the mean are more different for for and than for the other coefficients And Yeah And Yeah it the is There are strange strange thing happening with is that when you have different kind of noises the mean for the the silence portion is can be different Hmm And So when you look at the trajectory of has a strange shape and I was expecting th the s that these two mean helps especially because of the the strange shape uh which can like yo you can have um a trajectory for the speech and then when you are in the silence it goes somewhere but if the noise is different it goes somewhere else Oh So which would mean that if we estimate the mean based on all the signal even though we have frame dropping but we don't frame ev uh drop everything but uh this can hurts the estimation of the mean for speech and Mmm But I still have to investigate further I think Um a third thing is um that instead of t having a fixed time constant I try to have a time constant smaller at the beginning of the utterances to adapt more quickly to the r something closer to the right mean T t um Yeah And then this time constant increases and I have a threshold that well if higher than a certain threshold I keep it to this threshold to still uh adapt um the mean when if the utterance is uh long enough to to continue to adapt after like one second or Mmm Uh well this doesn't help neither but this doesn't hurt So well It seems pretty Wasn't there some experiment you were gonna try where you did something differently for each um uh I don't know whether it was each mel band or each uh um FFT bin or someth There was something you were gonna uh some parameter you were gonna vary depending on the frequency I don't know if that was I guess it was I don't know No u Maybe this this idea of having different normalization um For tunings for the different But Yeah I I thought Morgan you brought it up a couple meetings ago And then it was something about uh some and then somebody said yeah it does seem like you know is the one you know the major one or uh s I can't remember exactly what it was now Mmm Yeah There uh actually yeah S um very important to normalize and much less to normalize the other coefficients And um actu uh well at least with the current normalization scheme And we I think we kind of know that normalizing doesn't help with the current scheme And and Yeah In my idea I I was thinking that the the the reason is maybe because of these funny things that happen between speech and silence which have different means Um Yeah But maybe not so so Um easy to I I really would like to suggest looking um a little bit at the kinds of errors I know you can get lost in that and go forever and not see too much but sometimes but but um just seeing that each of these things didn't make things better may not be enough It may be that Yeah making them better in some ways and worse in others or increasing insertions and decreasing deletions or or um um you know helping with noisy case but hurting in quiet case And if you saw that then maybe you it would something would occur to you of how to deal with that Hmm Alright Mmm Yeah W um So it I think for the normalization Um Yeah been playing a little bit with some kind of thresholding and mmm as a first experiment I think I Yeah Well what I did is t is to take um to measure the average no the maximum energy of s each utterance and then put a threshold Well this for each mel band Then put a threshold fifteen DB below well uh a couple of DB below this maximum Mmm and Actually it was not a threshold it was just adding noise So I was adding a white noise energy uh fifteen DB below the maximum energy of the utterance And Yeah When we look at at the um MFCC that result from this they are a lot more smoother Um when we compare like a channel zero and channel one utterance um so a clean and uh the same noisy utterance well there is almost no difference between the cepstral coefficients of the two Hmm Um And Yeah And the result that we have in term of speech recognition actually not not worse not better neither Hmm but um kind of surprising that not worse because Sorry basically you add noise fifteen DB just fifteen DB below the maximum energy And