E: I sort of had forgotten to discuss it .
E: Um .
E: You always have to look at how this uh , these adjustments , uh , affect things .
E: And even though we 're not allowed to do that , again we maybe could reflect that back to our use of the features .
E: So if it if in fact , uh B: Mm - hmm .
E: The problem might be that the range of the MSG features is quite different than the range of the PLP or mel cepstrum .
B: Mm - hmm .
E: And you might wanna change that .
B: Mm - hmm .
B: But B: Yeah .
B: But , it 's d it 's after B: Well , it 's tandem features ,
B: so B: Mmm .
E: Yeah .
B: Yeah .
B: We we have estimation of post posteriors with PLP and with MSG as input ,
E: Yeah .
B: so I don
B: Well .
B: I don't know .
E: That means they 're between zero and one .
B: Mm - hmm .
E: But i it it it it doesn't necessarily E: You know , they could be ,
E: um E: Do - doesn't tell you what the variance of the things is .
B: Mmm .
B: Mm - hmm .
E: Right ?
E: Cuz if you 're taking the log of these things , it could be ,
E: uh E: Knowing what the sum of the probabilities are , doesn't tell you what the sum of the logs are .
B: Mm - hmm .
B: Yeah .
E: So .
B: Yeah .
B: So we should look at the likelihood ,
B: or or
B: what ?
B: Or B: well ,
B: at the log , perhaps ,
B: and E: Yeah .
B: Mm - hmm .
E: Yeah .
E: Or what you know , what you 're uh the thing you 're actually looking at .
B: Mm - hmm .
E: So your your E: the values that are are actually being fed into HTK .
B: Mm - hmm .
E: What do they look like ?
F: No
B: But F: And so th the , uh for the tandem system , the values that come out of the net don't go through the sigmoid .
F: Right ?
F: They 're sort of the pre - nonlinearity values ?
E: Right .
B: Yes .
E: So they 're kinda like log probabilities is what I was saying .
F: And those F: OK .
F: And tho that 's what goes into HTK ?
E: Uh , almost .
E: But then you actually do a KLT on them .
F: OK .
E: Um .
E: They aren't normalized after that ,
E: are they ?
B: Mmm . No ,
B: they are not E: No .
B: no .
E: OK .
E: So , um .
E: Right .
E: So the question is Yeah . Whatever they are at that point , um , are they something for which taking a square root or cube root or fourth root or something like that is is gonna be a good or a bad thing ?
E: So .
B: Mm - hmm .
E: Uh , and that 's something that
E: nothing nothing else after that is gonna E: Uh , things are gonna scale it E: Uh , you know , subtract things from it ,
E: scale it from it ,
E: but nothing will have that same effect .
E: Um .
E: So .
E: Um .
E: Anyway , eh F: Yeah .
F: Cuz if if the log probs that are coming out of the MSG are really big , the standard insertion penalty is gonna have very little effect
E: Well , the E: Right .
F: compared to , you know , a smaller set of log probs .
E: Yeah .
E: No . Again you don't really look at that .
E: It 's something that ,
E: and then it 's going through this transformation that 's probably pretty close to E: It 's , eh , whatever the KLT is doing .
E: But it 's probably pretty close to what a a a discrete cosine transformation is doing .
F: Yeah .
E: But still it 's it 's not gonna probably radically change the scale of things .
E: I would think .
E: And , uh E: Yeah .
E: It may be entirely off
E: and and it may be at the very least it may be quite different for MSG than it is for mel cepstrum or PLP .
E: So that would be E: So the first thing I 'd look at without adjusting anything would just be to go back to the experiment and look at the , uh , substitutions , insertions , and deletions .
E: And if the if
E: the , uh E: i if there 's a fairly large effect of the difference , say , uh , uh , the r ratio between insertions and deletions for the two cases then that would be , uh , an indicator that it might might be in that direction .
B: Mm - hmm .
B: Mm - hmm .
E: Anything else ?
B: Yeah . But ,
B: my my point was more that it it works sometimes
B: and E: Yeah .
B: but sometimes it doesn't work .
B: So .
E: Well .
B: And it works on TI - digits
B: and on SpeechDat - Car it doesn't work ,
B: and E: Yeah .
B: Mm - hmm .
B: Yeah .
B: Well .
E: But , you know , some problems are harder than others ,
B: Mm - hmm .
B: Yeah .
E: and E: And , uh , sometimes , you know , there 's enough evidence for something to work
E: and then it 's harder ,
E: it breaks .
E: You know ,
E: so it 's B: Mm - hmm .
E: But it but , um , i it it could be that when you say it works maybe we could be doing much better ,
E: even in TI - digits .
E: Right ?
B: Yeah .
E: So .
B: Yeah , sure .
B: Uh .
E: Hmm ?
E: Yeah .
B: Yeah .
B: Well ,
B: there is also the spectral subtraction ,
B: which ,
B: um B: I think maybe we should , uh , try to integrate it in in our system .
E: Yeah .
B: Mmm .
B: Mm - hmm .
E: Right .
B: But ,
E: O
B: I think that would involve to  to mmm use a big a al already a big bunch of the system of Ericsson .
B: Because he has spectral subtraction ,
B: then it 's followed by , um , other kind of processing that 's are dependent on the uh , if it 's speech or noi or silence .
E: Mm - hmm .
B: And there is this kind of spectral flattening after if it 's silence ,
B: and B: and s I I think it 's important , um , to reduce this musical noise and this this increase of variance during silence portions .
B: So .
B: Well .
B: This was in this would involve to take almost everything from from the this proposal
B: and B: and then just add some kind of on - line normalization in in the neural network .
B: Mmm .
E: OK .
E: Well , this 'll be , I think , something for discussion with Hynek next week .
B: Yeah .
B: Mm - hmm .
E: Yeah .
E: OK .
E: Right .
E: So .
E: How are , uh , uh how are things going with what you 're doing ?
D: Oh .
D: Well , um ,
D: I took a lot of time just getting my taxes out of the way D: multi - national taxes .
D: So , I 'm I 'm starting to write code now for my work
D: but I don't have any results yet .
D: Um , i it would be good for me to talk to Hynek , I think , when he 's here .
E: Yeah .
D: Do you know what his schedule will be like ?
E: Uh , he 'll be around for three days .
E: Uh , we 'll have a lot of time .
D: OK .
D: So , y
E: So , uh D: OK .
E: Um .
E: I 'll , uh E: You know , he 's E: he 'll E: he 'll be talking with everybody in this room
E: So .
F: But you said you won't you won't be here next Thursday ?
E: Not Thursday and Friday .
E: Yeah .
E: Cuz I will be at faculty retreat .
F: Hmm .
E: So .
E: I 'll try to connect with him and people as as I can on on Wednesday .
E: But E: Um .
E: Oh , how 'd taxes go ?
E: Taxes go OK ?
D: Mmm . Yeah .
E: Yeah . Oh , good . Yeah .
E: Yeah .
E: That 's just that 's that 's one of the big advantages of not making much money is the taxes are easier .
E: Yeah .
F: Unless you 're getting money in two countries .
E: I think you are .
F: They both want their cut .
E: Aren't you ?
B: Hmm .
D: Hmm . Yeah .
F: Right ?
E: Yeah .
E: Yeah .
E: Huh .
E: Canada w Canada wants a cut ?
D: Mm - hmm .
E: Have to do So you you have to do two returns ?
D: Mmm . W uh , for two thousand I did .
D: Yeah .
E: Oh , oh .
E: Yeah .
E: For tw
E: That 's right ,
E: ju
F: But not for this next year ?
E: Two thousand .
E: Yeah .
E: Probably not this next year , I guess .
D: Ye
E: Yeah .
D: Um .
E: Yeah .
D: Uh , I 'll I 'll still have a bit of Canadian income
D: but it 'll be less complicated
D: because I will not be a considered a resident of Canada anymore ,
D: so I won't have to declare my American income on my Canadian return .
E: OK .
E: Alright .
E: Uh .
E: Barry ,
E: do you wanna say something about your stuff here ?
A: Oh ,
A: um .
A: Right .
A: I just , um , continuing looking at , uh , ph uh , phonetic events ,
A: and , uh , this Tuesday gonna be , uh , meeting with John Ohala with Chuck to talk some more about these , uh , ph um , phonetic events .
A: Um , came up with , uh , a plan of attack ,
A: uh , gonna execute ,
A: and
A: um A: Yeah .
A: It 's that 's pretty much it .
E: Oh , well .
E: No
E: Um , why don't you say something about what it is ?
A: Oh ,
A: you oh , you want you want details .
A: Hmm .
A: OK .
E: Well , we 're all gathered here together .