temporal and spectral behaviors And the only thing you know consistent that we know about is that you want to get rid of the very low frequency component Hmm But do you really want to calculate the mean And you neglect all the silence regions or you just use everything twelve seconds and Um you do you mean in my tests Ye yeah Most of the silence has been cut out OK Just just silences And they are like pretty short Pretty Shor Yeah Yeah OK Yeah So you really need a lot of speech to estimate the mean of it Well if Yeah only use six seconds it still Yeah works pretty well I saw in my test before I was trying twelve seconds cuz that was the best in my OK before and that increasing past twelve seconds didn't seem to help Hmm Huh yeah I guess something I need to play with more to decide how to set that up for the SmartKom system Like may maybe if I trained on six seconds it would work better when I only had two seconds or four seconds and Yeah Yeah And um Yeah and again if you take this filtering perspective and if you essentially have it build up over time I mean if you computed means over two and then over four and over six essentially what getting at is a kind of uh ramp up of a filter anyway And so you may may just want to think of it as a filter But uh if you do that then um in practice somebody using the SmartKom system one would think if using it for a while it means that their first utterance instead of you know getting uh a forty percent error rate reduction get a uh over what uh get without this uh um policy uh you get thirty percent And then the second utterance that you give they get the full you know uh full benefit of it if this ongoing so thing you you cache the utterances how you get your uh M yeah Ah OK using a system to ask for directions or something OK you know say something first And and to begin with if it doesn't get them quite right ma m maybe come back and say excuse me uh or some I mean it should have some policy like that anyway And and uh uh in any event they might ask a second question And not like what doing doesn't uh improve things It does improve things just not as much as he would like And so uh a higher probability of it making an error uh in the first utterance What would be really cool is if you could have uh this probably users would never like this but if you had could have a system where before they began to use it they had to introduce themselves verbally You know Yeah my name is from And you could use that initial speech to do all these adaptations and Right Oh the other thing I guess which which uh I don't know much about as much as I should about the rest of the system but but um couldn't you uh if you if you sort of did a first pass I don't know what kind of uh uh capability we have at the moment for for doing second passes on on uh uh some kind of little small lattice or a graph or confusion network or something But if you did first pass with um the with either without the mean sub subtraction or with a a very short time one and then um once you uh actually had the whole utterance in if you did um the uh uh longer time version then based on everything that you had um and then at that point only used it to distinguish between you know top N um possible utterances or something you you might it might not take very much time I mean I know in the large vocabulary stu uh uh systems people were evaluating on in the past some people really pushed everything in to make it in one pass but other people didn't and had multiple passes And um the argument um against multiple passes was u u has often been but we want to this to be r you know have a nice interactive response And the counterargument to that which say uh BBN I think had was yeah but our second responses are second uh passes and third passes are really really fast So um if if your second pass takes a millisecond who cares Um S so um the the idea of the second pass would be waiting till you have more recorded speech Or Yeah so if it turned out to be a problem that you didn't have enough speech because you need a longer longer window to do this processing then uh one tactic is you know looking at the larger system and not just at the stuff is to take in um the speech with some simpler mechanism or shorter time mechanism um do the best you can and come up with some al possible alternates of what might have been said And uh either in the form of an list or in the form of a lattice or or confusion network or whatever And then the decoding of that is much much faster or can be much much faster if it isn't a big bushy network And you can decode that now with speech that actually processed using this longer time Mmm subtraction So I mean common that people do this sort of thing where they do more things that are more complex or require looking over more time whatever in some kind of second pass OK um and again if the second pass is really really fast Uh another one heard of is is in in connected digit stuff um going back and l and through backtrace and finding regions that are considered to be a d a digit but uh which have very low energy OK So uh I mean lots of things you can do in second passes at all sorts of levels Anyway throwing too many things out But So is that uh that it I guess it OK uh do you wanna go Sunil Yep Um so the last two weeks was like So been working on that Wiener filtering And uh found that uh s single like I just do a s normal Wiener filtering like the standard method of Wiener filtering And that doesn't actually give me any improvement over like I mean uh b it actually improves over the baseline but not like it doesn't meet something like fifty percent or something So been playing with Improves over the base line MFCC system Yeah Yeah So um So The improvement is somewhere around like thirty percent over the baseline Is that using in combination with something else No With just just one stage Wiener filter which is a standard Wiener filter