OK We seem to be recording Alright So crashing about not Number four not everything The lunch went a little later than I was expecting Chuck Hmm OK Chuck was telling too many jokes or something Yeah Pretty much OK Does anybody have an agenda No Well I sent a couple of items I thought of practical had I Yeah know if right if if too practical for what Yeah we only mean focused th on don't useless anything things practical Yeah No would don't we talk about practical things OK Well um I can give you an update on the transcription effort Great Uh maybe raise the issue of microphone uh um procedures with reference to the cleanliness of the recordings transcription uh microphone issues And then maybe ask th uh these guys The we have great great uh p steps forward in terms of the of the signal OK Well we have steps forward Well Yeah a Yes big would improvement this Yeah well OK Uh We You talk have about some the results Yeah of OK have a little use IRAM stuff but not sure if of general interest or not Uh bigram IRAM IRAM IRAM Well IRAM m bigram Bi Bigram Yeah know see where we are Hmm Um Since uh since I have to leave as usual at can we do the interesting stuff first I Well your pardon is I beg your the pardon stuff Th now Yeah you get to tell us the Please interesting specify But Yeah Well uh I guess the work been done on segmentation would I be think that would most a good thing to Yeah start with Um and um the other thing uh which just say very briefly that maybe relates to that a little bit which is that um uh one of the suggestions that came up in a brief meeting I had the other day when I was in Spain with uh Manolo Pardo and Javier uh Ferreiros who was here before was um why not start with what they had before but add in the boundaries So in what Javier did before when they were doing um h he was looking for uh speaker change points Um As a simplification he originally did this only using silence as uh a putative uh speaker change point Yeah And uh he did not say look at points where you were changing broad sp uh phonetic class for instance And for Broadcast News that was fine Here obviously not Yeah And um so one of the things that they were pushing in d in discussing with me is um w why are you spending so much time uh on the uh feature issue uh when perhaps if you sort of deal with what you were using before and then just broadened it a bit instead of just ta using silence as putative change point also So Nnn then yeah you already have the with Gaussians and H you know simple and so forth And you you might So there was a there was a little bit of a a a a difference of opinion because I I thought that it was interesting to look at what features are useful Yeah But uh on the other hand I saw that the they had a good point that uh if we had something that worked for many cases before maybe starting from there a little bit Because ultimately gonna end up with some Yeah s su kind of structure like that where you have some kind of simple HMM and testing the hypothesis that Yeah uh there is a change So so anyway I just reporting that But uh uh So Yeah why don't we do the Yeah discussion Do I I hear you you didn't OK Yeah Um so uh what we basically did so far was using the mixed file to to detect s speech or nonspeech portions in that And what I did so far is I just used our old Munich system which is an based system with Gaussian mixtures for s speech and nonspeech And it was a system which used only one Gaussian for silence and one Gaussian for speech And now I added uh possibility for for speech and nonspeech And I did some training on on one dialogue which was transcribed by Yeah Jose did a nons s transcription Adam Dave and I we did for that dialogue and I trained it on that And I did some for for Jane And not sure how good they are or what what the transcribers say They they can use it or Uh they they think a terrific improvement And um Hmm it real it just makes a a world of difference And um y you also did some something in addition which was um for those in which there was uh quiet speakers in the mix Yeah Uh yeah That that was one one one thing uh why I added more mixtures for for the speech So I saw that there were loud loudly speaking speakers and quietly speaking speakers And so I did two mixtures one for the loud speakers and one for the quiet And speakers who was loud and who was quiet did did that just for for five minutes of one dialogue Right and that was enough Yeah W train the system And What it it adapts uh on while running So What Hopefully kind OK of uh processing did you do just our our old Munich uh spectrum on mel scale twenty twenty critical bands and then loudness And four additional features which is energy loudness modified loudness and zero crossing rate So features Mmm And you also provided me with several different versions which I compared Yeah And so you change parameters What do you wanna say something about the Yeah parameters that you change You can specify the minimum length of speech or and silence portions which you want And so I did some some modifications in those parameters basically changing the minimum minimum length for s for silence to have er to have um yeah to have more or less uh silence portions in inserted So Right Yeah So this would work well for uh pauses and utterance boundaries Yeah and things like that But for overlap Yeah imagine that Yeah work at all that have plenty of Yeah Yeah sections that it are Yeah Yeah true Um But it it saves so much time the Yep transcribers just enormous enormous savings great Fantastic Um just qu one quickly uh still on the features So you have these features Yeah Uh a lot of them are spectral features Is there a a transformation uh like principal components transformation or something No Just It W IS two we originally we did that but we saw uh when we used it uh f for our microphone which yeah for our for our recognizer in Munich we saw that w not not so necessary It it works as well OK f with with without uh a LDA or something OK No I was j curious Yeah