D: That was , sort of the idea .
G: Mm - hmm .
B: Hmm .
B: Yeah
B: the that 's true .
G: I
B: That those those regions are the cause for this @ @ those negative values or whatever you get .
G: Mm - hmm .
G: Mm - hmm .
B: Yeah .
B: So .
G: I mean , we we could trit uh , we we could think how w what what we could try .
B: Yeah .
G: I mean , it it was just an idea .
B: Yeah , yeah .
E: Mm - hmm .
G: I mean , we D: I think when it 's noisy people should just speak up .
G: to G: Mmm .
B: So E: If we look at the France Telecom proposal , they use some kind of noise addition .
E: They have a random number generator , right ?
E: And they add noise on the trajectory of , uh , the log energy only , right ?
D: Oh , they do !
D: Oh .
B: Yep .
B: C - z C - zero and log energy also ,
E: Yeah .
B: yeah .
E: Um ,
E: But I don't know how much effect it this have ,
E: but they do that .
B: Now ?
E: Yeah .
G: Uh - huh .
B: Oh .
D: Hmm .
G: So it it it it it is l somehow similar to what E: I think because they have th log energy ,
E: yeah ,
E: and then just generate random number .
E: They have some kind of mean and variance ,
E: and they add this number to to the log energy simply .
E: Um D: To the l
B: Yeah B: the the log energy , the after the clean cleaning up .
E: Mm - hmm .
B: So they add a random random noise to it .
D: To the just the energy , or to the mel uh , to the mel filter ?
B: No .
B: On - only to the log energy .
E: Only E: Yeah .
D: Oh .
G: Uh - huh .
D: So it Cuz I mean , I think this is most interesting for the mel filters .
G: Uh - huh .
D: Right ?
D: Or or F F
D: one or the other .
G: But but they do not apply filtering of the log energy or what B: Like , uh I mean G: like like a spectral subtraction
G: or B: No their filter is not M domain .
G: Yeah .
B: S so they did filter their time signal
G: I kn
B: and then what @ @ u
G: And then they calculate from this , the log energy
G: or ?
B: Yeah B: then after that it is s almost the same as the baseline prop system .
G: Mm - hmm .
B: And then the final log energy that they that they get , that to the to that they add some random noise .
D: Yeah , but again , that 's just log energy as opposed to filter bank energy .
B: Yeah .
G: Mmm .
B: So it 's not the mel .
B: You know , it 's not the mel filter bank output .
D: Yeah .
G: Mm - hmm .
B: These are log energy computed from the time s domain signal ,
E: Mm - hmm .
B: not from the mel filter banks .
B: So D: Hmm .
E: Maybe it 's just a way to decrease the importance of this particular parameter in the in the world feature vector
B: did E: cu if you add noise to one of the parameters , you widen the distributions
D: Hmm .
B: Becomes flat .
B: The variance , yeah , reduces ,
E: and B: so .
B: Hmm , yeah .
E: Eee - sss - uh .
D: So it could reduce the dependence on the amplitude and so on .
D: Yeah .
E: Yeah .
B: Yeah .
B: Although D: Maybe .
F: So is , uh Is that about it ?
E: Mm - hmm .
B: Uh ,
F: Or ?
B: so the B: OK .
B: So the other thing is the I 'm just looking at a little bit on the delay issue where the delay of the system is like a hundred and eighty millisecond .
B: So I just just tried another sk system I mean , another filter which I 've like shown at the end .
B: Which is very similar to the existing uh , filter .
B: Only Uh , only thing is that the phase is is like a totally nonlinear phase
B: because it 's a it 's not a symmetric filter anymore .
F: This is for the LDA ?
B: Yeah B: so so this this is like So this makes the delay like zero for LDA
B: because it 's completely causal .
F: Oh .
B: So B: So I got actually just the results for the Italian for that
B: and that 's like B: So the fifty - one point O nine has become forty - eight point O six ,
B: which is like three percent relative degradation .
E: Mm - hmm .
B: So I have like the fifty - one point O nine
B: and B: So .
B: I don't know it f fares for the other conditions .
B: So it 's just like it 's like a three percent relative degradation ,
B: with the G: But but is there is there a problem with the one hundred eighty milliseconds ?
G: Or ?
D: Th - Well , this is B: u Uh , may
G: Yeah , I mean , I talked to to uh , I ta Uh , I talked , uh , about it with with Hynek .
G: I mean , there is D: This is D: So So , basically our our position is that , um , we shouldn't be unduly constraining the latency at this point
D: because we 're all still experimenting with trying to make the performance better in the presence of noise .
D: Uh , there is a minority in that group who is a arguing who are arguing for um , uh , having a further constraining of the latency .
D: So we 're s just continuing to keep aware of what the trade - offs are and , you know , what what do we gain from having longer or shorter latencies ?
G: Mmm .
D: But since we always seem to at least get something out of longer latencies not being so constrained , we 're tending to go with that if we 're not told we can't do it .
G: Mm - hmm .
F: What where was the , um the smallest latency of all the systems last time ?
B: The French Telecom .
D: Well , France Telecom was was was very short latency
G: It 's D: and they had a very good result .
F: What what was it ?
D: It was thirty - five .
G: It was in the order of thirty milliseconds
G: or D: Yeah .
F: Thirteen ?
D: th th
G: Thirty .
F: Thirty .
B: Thirty - four .
D: Yeah .
G: Yeah .
D: Yeah , so it 's possible to get very short latency .
D: But , again , we 're the the approaches that we 're using are ones that take advantage of F: Yeah .
F: I was just curious about where we are compared to , you know , the shortest that people have done .
G: But but I think this thirty milliseconds they they did it did not include the the delta calculation .
B: Yeah .
G: And this is included now ,
B: Yeah .
B: Yeah .
B: Yeah .
G: you know ?
B: Yeah .
B: So if they include the delta , it will be an additional forty millisecond .
E: Mm - hmm .
G: Yeah .
D: Yeah .
G: I I don't remember the G: i th They were not using the HTK delta ?
B: No , they 're using a nine - point window ,
B: which is like a four on either side ,
G: Nine - point .
B: which is like G: OK .
B: f so G: Mmm .
B: they didn't include that .
D: Yeah .
G: Mm - hmm .
B: So F: OK .
E: Where does the comprish compression in decoding delay comes from ?
B: That 's the way the the the frames are packed ,
B: like you have to wait for one more frame to pack .
B: Because it 's the CRC is computed for two frames always .
E: Mm - hmm .
D: Well , that the they would need that forty milliseconds also .
D: Right ?
B: No .
B: They actually changed the compression scheme altogether .
E: Mm - hmm .
B: So they have their own compression and decoding scheme
B: and they I don't know what they have .
D: Oh .
B: But they have coded zero delay for that .
B: Because they ch I know they changed it ,
B: their compression .
B: They have their own CRC ,
B: their their own error correction mechanism .
D: Oh .
B: So they don't have to wait more than one more frame to know whether the current frame is in error .
D: Oh , OK .
B: So they changed the whole thing so that there 's no delay for that compression and part also .
D: Hmm .
G: Mm - hmm .
B: Even you have reported actually zero delay for the compression .
B: I thought maybe you also have some different G: Mmm .
G: Mmm .
G: No , I think I I used this scheme as it was before .
B: OK .
B: Ah .
B: Mm - hmm .
F: OK ,
F: we 've got twenty minutes
F: so we should probably try to move along .
F: Uh , did you wanna go next , Stephane ?
E: I can go next .
E: Yeah .
E: Mmm .
D: Oh .
E: It 's D: Wait a minute .
D: It 's E: Yeah ,
E: we have to take D: Wait a minute .
D: I think I 'm confused .
E: Well E: OK .
D: Alright .
E: So you have w w one sheet ?
E: This one is you don't need it ,
E: alright .
D: Uh E: So you have to take the whole the five .
E: There should be five sheets .
D: OK ,
D: I have four now
D: because I left one with Dave because I thought I was dropping one off and passing the others on .
D: So , no ,
D: we 're not .
D: OK .
B: Thanks .
H: Please give me one .
D: Ah , we need one more over here .
E: OK , maybe there 's not enough for everybody .
F: I can share with Barry .
E: But A: Yeah .
G: OK .
D: Oh , OK .
E: Can we look at this ?
C: Yeah .
E: So ,
E: yeah , there are two figures showing actually the , mmm , um , performance of the current VAD .
E: So it 's a n neural network based on PLP parameters ,
E: uh , which estimate silence probabilities ,
E: and then I just put a median filtering on this
E: to smooth the probabilities , right ?
E: Um E: I didn't use the the scheme that 's currently in the proposal
E: because I don't want to E: In the proposal E: Well , in in the system we want to add like speech frame before every word and a little bit of of , uh , s a couple of frames after also .
E: Uh , but to estimate the performance of the VAD , we don't want to do that ,
E: because it would artificially increase the um the false alarm rate of speech detection .
E: Right ?
E: Um ,
E: so ,
E: there is u normally a figure for the Finnish and one for Italian .
E: And maybe someone has two for the Italian
E: because I 'm missing one figure here .
B: No .
E: Well E: Well , whatever .
E: Uh Yeah , so one surprising thing that we can notice first is that apparently the speech miss rate is uh , higher than the false alarm rate .
E: So .
G: So so what is the lower curve and the upper curve ?
E: It means E: Mm - hmm .
E: Yeah , there are two curves .
G: Yeah .
E: One curve 's for the close - talking microphone , which is the lower curve .
G: Ah , OK .
E: And the other one is for the distant microphone
E: which has more noise
E: so ,
E: it 's logical that it performs worse .
E: So as I was saying , the miss rate is quite important
E: uh , which means that we tend to label speech as as a silence .
E: And ,
E: uh , I didn't analyze further yet ,
E: but I think it 's it may be due to the fricative sounds
E: which may be in noisy condition maybe label labelled as silence .
E: And it may also be due to the alignment
E: because E: well , the reference alignment .
E: Because right now I just use an alignment obtained from from a system trained on channel zero .
E: And
E: I checked it a little bit
E: but there might be alignment errors .
E: Um , yeah ,
E: e
E: like the fact that  the the models tend to align their first state on silence and their last state o on silence also .
E: So the reference reference alignment would label as speech some silence frame before speech and after speech .
E: This is something that we already noticed before
E: when E: mmm ,
E: So this cus this could also explain , uh , the high miss rate maybe .
G: And and this this curves are the average over the whole database ,
E: Uh G: so .
E: Yeah .
E: Right .
G: Mmm .
E: Um E: Yeah ,
E: and the different points of the curves are for five uh , thresholds on the probability <comment> uh from point three to point seven .
B: So that threshold E: Mm - hmm .
E: Yeah .
B: OK .
B: S OK B: so d the detection threshold is very E: So the v
E: The VAD ?
B: Yeah , yeah .
E: Yeah .
E: There first , a threshold on the probability <comment> @ @ <comment> That puts all the values to zero or one .
B: Mmm .
E: And then the median filtering .
B: Yeah ,
B: so the median filtering is fixed .
B: You just change the threshold ?
E: Yeah .
E: It 's fixed ,
E: yeah .
B: Yeah .
E: Mm - hmm .
E: So , going from channel zero to channel one , uh , almost double the error rate .
E: Um ,
E: Yeah .
E: Well , so it 's a reference performance that we can you know , if we want to to work on the VAD , <comment> we can work on this basis
B: Mm - hmm .
E: and B: OK .
A: Is this is this VAD a MLP ?
E: Yeah .
A: OK .
A: How how big is it ?
E: It 's a very big one .
E: I don't remember .
B: So three three hundred and fifty inputs ,
E: m
B: uh ,
B: six thousand hidden nodes and two outputs .
B: t t
A: OK .
B: Yeah .
E: Mm - hmm .
D: Middle - sized one .
B: Yeah .
E: Mm - hmm .
E: Yeah .
E: Uh , ppp .
E: I don't know , you have questions about that , or suggestions ?
B: Mmm .
B: S so E: It seems the performance seems worse in Finnish ,
E: which B: Well , it 's not trained on Finnish .
E: uh H: It 's worse .
E: It 's not trained on Finnish ,
E: yeah .
D: What 's it trained on ?
B: I mean , the MLP 's not trained on Finnish .
D: Right ,
D: what 's it trained on ?
B: Oh oh .
B: Sorry .
B: Uh , it 's Italian TI - digits .
D: Yeah .
D: Oh , it 's trained on Italian ?
B: Yeah .
D: Yeah ,
E: Mm - hmm .
D: OK .
E: And B: That 's right .
D: OK .
E: And also there are like funny noises on Finnish
E: more than on Italian .
G: Mm - hmm .
E: I mean , like music
B: Yeah .
E: and um B: Yeah , the Yeah , it 's true .
E: So , yeah ,
E: we were looking at this .
E: But for most of the noises , noises are E: um ,
E: I don't know if we want to talk about that .
E: But ,
E: well , the the " Car " noises are below like five hundred hertz .
E: And we were looking at the " Music " utterances
E: and in this case the noise is more about two thousand hertz .
E: Well , the music energy 's very low apparently .
B: Yeah .
E: Uh ,
E: uh , from zero to two two thousand hertz .
E: So maybe just looking at this frequency range for from five hundred to two thousand would improve somewhat the VAD
B: Mmm .
E: and B: Yeah .
E: Mmm B: So there are like some some s some parameters you wanted to use or something ?
E: Yeah ,
E: but E: Yes .
B: Or Yeah .
E: Mm - hmm .
E: Uh , the next ,
E: um G: So is the is the is the training is the training based on these labels files which you take as reference here ?
E: Oh , it 's there .
G: Wh - when you train the neural net y y you E: Yeah .
E: No .
E: It 's not .
E: It 's it was trained on some alignment obtained
E: um ,
E: uh For the Italian data , I think we trained the neural network on with embedded training .
E: So re - estimation of the alignment using the neural network , I guess .
E: That 's right ?
B: Yeah .
B: We actually trained , uh , the on the Italian training part .
E: Yeah .
B: We we had another system with u
E: So it was a f f a phonetic classification system for the Italian Aurora data .
B: Yeah .
B: It must be somewhere .
B: Yeah .
E: For the Aurora data that it was trained on , it was different .
E: Like , for TI - digits you used a a previous system that you had , I guess .
B: What B: No it B: Yeah , yeah .
B: That 's true .
E: So the alignments from the different database that are used for training came from different system .
B: Syste
B: Yeah .
E: Then we put them tog together .
E: Well , you put them together and trained the VAD on them .
B: Yeah .
E: Mmm .
B: Yeah .
G: Hmm .
E: Uh ,
E: But did you use channel did you align channel one also ?
E: Or B: I just took their entire Italian training part .
E: Yeah .
B: So it was both channel zero plus channel one .
E: So di
E: Yeah .
E: So the alignments might be wrong then on channel one , right ?
B: On one .
B: Possible .
E: So we might ,
E: yeah ,
B: We can do a realignment .
B: That 's true .
E: at least want to retrain on these alignments ,
E: which should be better because they come from close - talking microphone .
G: Yeah ,
G: the that was my idea .
G: I mean , if if it ha if it is not the same labeling which is taking the spaces .
B: Yeah .
E: OK .
B: Yeah , possible .
E: Yeah .
G: Mmm .
B: I mean , it E: Yeah .
B: so the system B: so the VAD was trained on maybe different set of labels for channel zero and channel one
E: Mm - hmm .
B: and G: Mm - hmm .
E: Mm - hmm .
B: was the alignments were w were different for s certainly different because they were independently trained .
B: We didn't copy the channel zero alignments to channel one .
E: Mm - hmm .
G: Mm - hmm .
B: Yeah .
E: Yeah .
B: But for the new alignments what you generated , you just copied the channel zero to channel one , right ?
E: Right .
E: Yeah .
B: Yeah .
E: Um .
E: And eh , hhh
E: actually when we look at at the VAD , for some utterances it 's almost perfect ,
E: I mean , it just dropped one frame ,
E: the first frame of speech
E: or E: So there are some utterances where it 's almost one hundred percent VAD performance .
G: Hmm .
E: Uh ,
E: but Yeah .
E: Mmm E: Yep .
E: So the next thing is um , I have the spreadsheet for three different system .
E: But for this you only have to look right now on the SpeechDat - Car performance
E: uh , because I didn't test E: so I didn't test the spectral subtraction on TI - digits yet .
E: Uh , so you have three she sheets .
E: One is the um proposal - one system .
E: Actually , it 's not exe exactly proposal - one .
E: It 's the system that Sunil just described .
E: Um ,
E: but with uh , Wiener filtering from um , France Telecom included .
E: Um , so this gives like fifty - seven point seven percent , uh , s uh , error rate reduction on the SpeechDat - Car data .
E: Mmm ,
E: and then I have two sheets where it 's for a system where E: uh ,
E: so it 's again the same system .
E: But in this case we have spectral subtraction
E: with a maximum overestimation factor of two point five .
E: Uh , there is smoothing of the gain trajectory with some kind of uh , low - pass filter ,
E: which has forty milliseconds latency .
E: And then , after subtraction um , I add a constant to the energies
E: and I have two cases d where The first case is where the constant is twenty - five DB below the mean speech energy
E: and the other is thirty DB below .
E: Um ,
E: and for these s two system we have like fifty - five point , uh , five - percent improvement ,
E: and fifty - eight point one .