C: an and what are we talking about here ?
C: Is this TI - digits
B: It 's the It - it 's Italian .
C: or B: I 'm talking about Italian ,
C: Italian ?
B: yeah .
C: And what did C: So , what was the , um , uh , corresponding number , say , for , um , uh , the Alcatel system for instance ?
B: Mmm .
C: Do you know ?
D: Yeah ,
D: so it looks to be , um B: You have it ?
D: Yep ,
D: it 's three point four ,
D: uh , eight point , uh , seven ,
D: and , uh , thirteen point seven .
C: OK .
B: Yep .
C: OK .
B: So Thanks .
D: Mm - hmm .
C: OK .
B: So ,
B: uh , this is the single stage Wiener filter ,
B: with The noise estimation was based on first ten frames .
C: Mm - hmm .
B: Actually I started with using the VAD to estimate the noise
B: and then I found that it works B: it doesn't work for Finnish and Spanish
B: because the VAD endpoints are not good to estimate the noise
B: because it cuts into the speech sometimes ,
B: so I end up overestimating the noise and getting a worse result .
C: Mm - hmm .
B: So it works only for Italian by u for using a VAD to estimate noise .
B: It works for Italian because the VAD was trained on Italian .
C: Mm - hmm .
B: So , uh B: so this was , uh B: And so this was giving B: um ,
B: this this was like not improving a lot on this baseline of not having the Wiener filter on it .
B: And ,
B: so ,
B: uh , I ran this stuff with one more stage of Wiener filtering on it
B: but the second time , what I did was I estimated the new Wiener filter based on the cleaned up speech , and did , uh , smoothing in the frequency to to reduce the variance C: Mm - hmm .
B: I mean , I have I 've I 've observed there are , like , a lot of bumps in the frequency when I do this Wiener filtering
B: which is more like a musical noise or something .
B: And so by adding another stage of Wiener filtering , the results on the SpeechDat - Car was like ,
B: um B: So , I still don't have the word error rate .
B: I 'm sorry about it .
B: But the overall improvement was like fifty - six point four six .
B: This was again using ten frames of noise estimate and two stage of Wiener filtering .
B: And the rest is like the LDA plu and the on - line normalization all remaining the same .
B: Uh ,
B: so this was , like , compared to , uh , uh Fifty - seven is what you got by using the French Telecom system ,
B: right ?
D: No , I don't think so .
D: Is it on Italian ?
B: Y i
B: No ,
B: this is over the whole SpeechDat - Car .
B: So D: Oh , yeah , fifty - seven B: point D: Right .
B: Yeah ,
B: so the new the new Wiener filtering schema is like some fifty - six point four six
B: which is like one percent still less than what you got using the French Telecom system .
D: Uh - huh .
D: Mm - hmm .
C: But it 's a pretty similar number in any event .
B: It 's very similar .
C: Yeah .
C: But again , you 're you 're more or less doing what they were doing ,
C: right ?
B: It 's it 's different in a sense like
B: I 'm actually cleaning up the cleaned up spectrum
B: which they 're not doing .
B: They 're d what they 're doing is , they have two stage stages of estimating the Wiener filter ,
C: Yeah .
B: but the final filter , what they do is they they take it to their time domain by doing an inverse Fourier transform .
C: Uh - huh .
B: And they filter the original signal using that fil filter ,
B: which is like final filter is acting on the input noisy speech rather than on the cleaned up .
B: So this is more like I 'm doing Wiener filter twice ,
B: but the only thing is that the second time I 'm actually smoothing the filter and then cleaning up the cleaned up spectrum first level .
C: OK .
B: And so that that 's that 's what the difference is .
B: And actually I tried it on s the original clean I mean , the original spectrum where , like , I the second time I estimate the filter but actually clean up the noisy speech rather the c s first output of the first stage
B: and that doesn't seems to be a giving , I mean , that much improvement .
B: I I didn didn't run it for the whole case .
B: And B: and what I t what I tried was , by using the same thing
B: but Uh ,
B: so we actually found that the VAD is very , like , crucial .
B: I mean , just by changing the VAD itself gives you the a lot of improvement
C: Mm - hmm .
B: by instead of using the current VAD , if you just take up the VAD output from the channel zero , <comment> when instead of using channel zero and channel one ,
B: because that was the p that was the reason why I was not getting a lot of improvement for estimating <comment> the noise .