Uh so um that uh that seemed to be So if you have that that better recognizer that can that can build up more parameters and if you um have the natural room which in this case has a p a pretty bad ratio then in that case um the right thing to do is just do u use speaker adaptation And and not bother with with this acoustic uh processing But I think that that would not be true if we did some explicit as well as uh the convolutional kind of things we were doing So Hmm what we found I um uh started working on the uh Mississippi State recognizer Oh So OK got in touch with Joe and and uh from your email and things like that And uh they added me to the list uh great mailing list And he gave me all of the pointers and everything that I needed And so I downloaded the um There were two things uh that they had to download One was the uh I guess the software And another wad was a um sort of like a sample a sample run So I downloaded the software and compiled all of that And it Eight compiled fine Oh problems And um I grabbed the sample stuff but I haven't uh That sample was compiled released only yesterday or the day before right No Well I haven't grabbed that one yet So two Oh there is another short sample set another o short one sample yeah OK And so I haven't grabbed the latest one that he just Oh OK put out yet F Yeah OK So Um but the software seemed to compile fine and everything so And um So Is there any word yet about the issues about um adjustments for different feature sets or anything No I I d You asked me to write to him and I think I forgot to ask him about that Yeah I did ask him he didn't reply I I don't remember yet Uh d double check that and Yeah ask him again Hmm Mmm that could r turn out to be an important issue for Yeah us Yeah Yeah Cuz they have Maybe it it to the list Yeah Cuz they have uh already frozen those in i insertion penalties and all those stuff is what I feel Because they have this document explaining the recognizer And they have these tables with uh various language model weights insertion penalties u haven't seen that one yet Uh So th there on that OK web And uh on that I mean they have run some experiments using various insertion penalties and all And those picked Yeah I think values yeah they OK values OK For r w what test set Uh p the one that they have reported is a NIST evaluation Wall Street Journal But that has nothing to do with You what know testing on No right So like um So they are actually trying to uh fix that those values using the clean uh training part of the Wall Street Journal Which is I mean the Aurora Aurora has a clean subset I mean Right and then this going to run some evaluations set setting it based on that Yeah OK So now we may come back to the situation where we may be looking for a modification of the features to account for the fact that Yeah can't modify these parameters But Yeah um uh but still worth I think just since you know just chatting with Joe about the issue Yeah OK Do Um something I should just send to him or do you think I should send it to this an a m a mailing list not a secret I mean you know certainly willing to talk about it with everybody but I think I think that um um probably best to start talking with him just to OK Uh you know a dialogue between two of you about what you know what does he think about Yeah this and what what you know what could be done about it Um OK if you get ten people in involved in it be a lot of perspectives based on you Yeah you know Right Uh But I mean I think it all should come up eventually but OK if if there is any uh uh way to move in a way that would that would you know be more open to different kinds of features But if if uh if there isn't and just kind of shut down and and then also probably not worthwhile bringing it into a larger forum where Yeah where political issues will come in OK Oh So this is now compiled under Solaris Yeah Yeah OK Because Yep he there was some mail r saying that may Yeah stable for Linux and all those Yeah i that was a particular version SUSI Yeah yeah SUSI or whatever Yeah was yeah we don't have OK that So OK Should fine be OK Yeah Yeah it compiled fine actually No no errors Nothing So good this is slightly off topic but uh I noticed just glancing at the uh Hopkins workshop uh web site that uh um one of the thing I don't know Well see how much they accomplish but one of the things that they were trying to do in the graphical models thing was to put together a a uh tool kit for doing uh r um arbitrary graphical models for uh speech recognition Hmm So And Jeff uh the two Jeffs were the second Jeff Uh Oh uh do you know Geoff Zweig No Oh Uh he he uh he was here for a couple years and Oh he uh got his PHD He And uh been at IBM for the last couple years Oh OK So Wow Uh That so would he be did neat he did his PHD on dynamic uh for for speech recognition He had some continuity built into the model presumably to handle some um inertia in the in the production system and um So Hmm Um been playing with first the um VAD Um so exactly the same approach but the features that the VAD neural network use are uh MFCC after noise compensation Oh I think I have the results What was it using before Before it was just So Yeah it was actually No Not I mean it was just the noisy features I guess Yeah Yeah yeah noisy yeah not compensated features Um This is what we get after This So actually we yeah here the features are noise compensated and there is also the LDA filter Um and then a pretty small neural network which use um nine frames of of six features from to plus the first derivatives And it has one hundred hidden units Is that nine frames u s uh centered around the current frame Yeah Or S so sorry how many how many inputs So twelve times nine Twelve times nine inputs and a hundred uh hidden Hidden and Two outputs two outputs