wi is um the choice of the analysis window length So just been using two seconds just because what Carlos did before Uh I wrote to him asking about he chose the two seconds And it seemed like he chose it a bit informally So um with the with the HTK I should be able to do some experiments on just varying that length say between one and three seconds in a few different reverberation conditions um say this room and also a few of the artificial impulse responses we have for reverberation just um making some plots and seeing how they look And um so with the the sampling rate I was using one second or two seconds or four seconds is at a power of two um number of samples and um jus f for the ones in between I guess just I guess one thing that might also be an issue uh cuz part of what doing is getting a a spectrum over a bunch of different kinds of speech sounds Um and so it might matter how fast someone was talking for instance Oh You know if you if if if a lot of phones in one second maybe get a a really good sampling of all these different things and and uh on the other hand if talking slowly maybe need more So I don't Huh you have some samples of faster or slower speech but it might make a difference I don't know Uh yeah I don't I don't think the data that I have um i is would be appropriate for probably that not Yeah But what do you What about if I w I fed it through some kind of um speech processing algorithm that changed the speech rate Yeah but then have the degradation of of uh whatever you do uh added onto that But maybe Yeah maybe if you get something that sounds that does a pretty job at that Yeah Well uh just could if imagine think that worth looking into I mean it it is getting a little away from reverberation Um yeah just that making a choice uh I was thinking more from the system aspect if making Yeah choice for SmartKom that that that it might be that it c the optimal number could be different Right depending on Could be I don't know And and th the third thing um uh is um Barry explained LDA filtering to me yesterday And so um Mike Shire in his thesis um did a a series of experiments um training LDA filters in d on different conditions And you were interested in having me repeat this for for this mean subtraction approach Is is that right Or for these long analysis windows I guess is the right way to put it I guess the the the issue I was the general issue I was bringing up was that if have a moving moving window uh a wa a a set of weights times things that uh move along shift along in time that you have in fact a linear time invariant filter And you just happened to have picked a particular one by setting all the weights to be equal And so the issue is what are some other filters that you could use uh in that sense of filter And um as I was saying I think the simplest thing to do is not to train anything but just to do some sort of uh uh hamming or Hanning Right uh kind of window kind of thing just sort of to the jarring So I think that would sort of be the first thing to do But then yeah the LDA i uh is interesting because it would sort of say well suppose you actually trained this up to do the best you could by some criterion what would the filter look like then Uh and um sort of what doing in this Aur Aurora stuff And uh still not clear to me in the long run whether the best thing to do would be to do that or to have some stylized version of the filter that looks like these things trained up because you always have the problem that trained up for one condition and it isn't quite right for another So uh why why RASTA filter has actually ended up lasting a long time people still using it quite a bit because y you don't change it So doesn't get any worse Uh Huh Anyway o OK So um a actually I was just thinking about what I was asking about earlier wi which is about having less than say twelve seconds in the SmartKom system to do the mean subtraction You said in systems where you use cepstral mean subtraction they concatenate utterances and do you know how they address this issue of um testing versus training Can I think ahead they do is they do it always I mean that you just take what you have from the past that you calculate the mean of this and subtract the mean OK Um And then you can yeah you you can increase your window whi while you get while you are getting more samples OK um and um so so in tha in that case wh what do they do when t um performing the cepstral mean subtraction on the training data So because have hours and hours of training data So do they cut it off and start over At intervals Or So do you have uh you you mean you have files which are hours of hours long Or Oh well no I guess not Yeah But I mean usually you have in the training set you have similar conditions I mean file lengths are I guess the same order or in the same size as for test data or aren't they OK But OK So if interacting with the system though uh Morgan uh Morgan said that you would tend to um chain utterances together um think what r was s I thought what I was saying Oh was that um at any given point you are gonna start off with what you had from before From and so if splitting things up into utterances So for instance in a dialogue system where gonna be asking uh you know th for some information some initial th something And you know the first time out you you might have some general average But you you d you don't have very much information yet But at after given one utterance got something You can compute your mean cepstra from that and then can use it for the next thing that they say uh so that you know the performance should be better that second time Um and I think the heuristics of exactly how people handle that and how they handle their training sure vary