E: in order to calculate the estimation of the clean speech given our noisy speech .
B: So , I 'm I 'm not following this perfectly
B: but , um ,
B: I B: Are you saying that all of these estimates are done using , um , estimates of the probability density for the noise that are calculated only from the first ten frames ?
E: Yeah .
B: And never change throughout anything else ?
E: Never cha
E: This is one of the approximations that I am doing .
B: Per per per utterance ,
B: or per ?
E: Per utterance . Yes .
B: Per utterance .
E: Per utterance . Yes .
B: OK .
B: So it 's done it 's done new for each new utterance .
E: And th
E: Yeah .
B: So this changes the whole mapping for every utterance .
E: It 's not E: Yeah .
E: Yeah .
E: It 's fixed , the dictionary .
B: OK .
E: And the other estimation is when I do the uh on - line estimation , I change the means and variance of th for the noisy speech
B: OK .
B: Yeah ?
E: each time that I detect noise .
B: Mm - hmm .
E: I do it uh again this
E: develop .
E: Estimate the new mean and the variance of the noisy speech .
E: And with th with this new s new mean and variance I estimate again this .
B: So you estimated , uh , f completely forgetting what you had before ?
E: Um ,
B: Uh , or is there some adaptation ?
E: no , no , no . It 's not completely E: No , it 's I am doing something like an adaptation of the noise .
B: OK .
B: Now do we know , either from their experience or from yours , that , uh , just having , uh , two parameters , the the mean and variance , is enough ?
B: Yeah .
B: I mean , I know you don't have a lot of data to estimate with ,
B: but but , uh ,
B: um E: I estimate mean and variance for each one of the Gaussian of the codebook .
B: No , I 'm talking about the noise .
E: Oh ,
E: um . Well , only one B: There 's only one Gaussian .
E: I am only using only one .
B: Right .
E: I don't know i
B: And you and and it 's ,
B: uh , uh B: right ,
B: it 's only B: it 's only one B: Wait a minute .
B: This is B: what 's the dimensionality of the Gaussian ?
B: This is E: Uh , it 's in after the mel filter bank .
B: So this is twenty or something ?
E: Twenty - three .
B: Twenty ?
B: So it 's B: Yeah . So it 's actually forty numbers that you 're getting .
B: Yeah , maybe maybe you don't have a E: Uh , the original paper say that only one Gaussian for the noise .
B: Well , yeah .
B: But , I mean , no no paper is is a Bible ,
E: Yeah , maybe isn't the right thing .
B: you know .
B: This is this is , uh E: Yeah , yeah , yeah .
B: The question is , um , whether it would be helpful , i particularly if you used if you had more B: So , suppose you did B: This is almost cheating .
B: It certainly isn't real - time .
B: But if y suppose you use the real boundaries that that you were in fact were given by the VAD and so forth
B: or I I guess we 're gonna be given even better boundaries than that .
B: And you look you take all o all of the nonspeech components in an utterance ,
B: so you have a fair amount .
B: Do you benefit from having a better model for the noise ?
B: That would be another question .
E: Maybe .
B: So first question would be to what extent i are the errors that you 're still seeing based on the fact that you have poor boundaries for the , uh , uh , nonspeech ?
B: And the second question might be , given that you have good boundaries , could you do better if you used more parameters to characterize the noise ?
B: Um .
B: Also another question might be B: Um , they are doing they 're using first term only of the vector Taylor series ?
E: Yeah .
B: Um , if you do a second term does it get too complicated cuz of the nonlinearity ?
E: Yeah . It 's quite complicated .
B: Yeah , OK .
B: No , I won't ask the next question then .
E: Oh , it 's it 's the for me it 's the first time that I am working with VTS .
E: Uh B: Yeah .
B: No , it 's interesting .
B: Uh , w we haven't had anybody work with it before ,
B: so it 's interesting to get your get your feedback about it .
E: It 's another type of approximation because i because it 's a statistic statistic approximation to remove the noise .
E: I don't know .
B: Right .
F: Great .
F: OK .
F: Well , I guess we 're about done .
F: Um ,
F: so some of the digit forms don't have digits .
F: Uh , we ran out
F: there were some blanks in there ,
F: so not everybody will be reading digits .
F: But , um ,
F: I guess you 've got some . Right , Morgan ?
B: I have some .
F: So ,
F: why don't you go ahead and start .
F: And I think it 's just us down here at this end that have them .
D: S
E: um
F: So .
B: Uh , OK .
D: S so , we switch off with this
F: Whenever you 're ready .
D: or n ?
F: Uh , leave it on ,
D: No . OK .
F: uh ,
B: They prefer to have them on
F: and the B: just so that they 're continuing to get the distant , uh , information .
F: Yeah .
D: OK .
D: OK .
F: OK .
B: OK . S