D: And and what is that ?
D: I mean , you say " not clear yet " .
D: What what do you mean ?
A: Uh , mmm , uh , <comment> I mean , that the the fact that s Well , for for TI - digits the TIMIT net is the best ,
A: which is the English net .
D: Mm - hmm .
A: But the other are slightly worse .
A: But you have two two effects , the effect of changing language
A: and the effect of training on something that 's Viterbi - aligned instead of hand hand - labeled .
B: Yeah .
A: So . Um .
A: Yeah .
D: Do you think the alignments are bad ?
D: I mean , have you looked at the alignments at all ?
D: What the Viterbi alignment 's doing ?
A: Mmm .
A: I don't I don't know .
A: Did - did you look at the Spanish alignments Carmen ?
B: Mmm , no .
D: Might be interesting to look at it .
D: Because , I mean , that is just looking
D: but um , um It 's not clear to me you necessarily would do so badly from a Viterbi alignment .
D: It depends how good the recognizer is
A: Mm - hmm .
D: that 's that the the engine is that 's doing the alignment .
A: Yeah . But Yeah . But , perhaps it 's not really the the alignment that 's bad
A: but the just the ph phoneme string that 's used for the alignment
D: Aha !
B: Yeah .
A: Mmm .
D: The pronunciation models and so forth
A: I mean for We A: It 's single pronunciation ,
A: uh D: Aha .
D: I see .
A: French French s uh , phoneme strings were corrected manually
A: so we asked people to listen to the um the sentence
A: and we gave the phoneme string and they kind of correct them .
A: But still ,
A: there there might be errors just in the in in the ph string of phonemes .
A: Mmm .
A: Um .
A: Yeah , so this is not really the Viterbi alignment ,
A: in fact ,
A: yeah .
A: Um , the third The third uh issue is the noise dependency perhaps
A: but , well , this is not clear yet
A: because all our nets are trained on the same noises
A: and D: I thought some of the nets were trained with SPINE and so forth .
D: So it And that has other noise .
A: Yeah .
A: So A: Yeah .
A: But A: Yeah .
A: Results are only coming for for this net .
A: Mmm .
D: OK , yeah , just don't just need more more results there with that @ @ .
A: Yeah .
A: Um .
A: So . Uh , from these results we have some questions with answers .
A: What should be the network input ?
A: Um , PLP work as well as MFCC , I mean .
A: Um .
A: But it seems impor important to use the delta .
A: Uh , with respect to the network size ,
A: there 's one experiment that 's still running
A: and we should have the result today ,
A: comparing network with five hundred and one thousand units .
A: So ,
A: nnn , still no answer actually .
D: Hm - hmm .
A: Uh , the training set ,
A: well , some kind of answer .
A: We can , we can tell which training set gives the best result ,
A: but we don't know exactly why .
D: Uh . Right , I mean the multi - English so far is is the best .
A: Uh , so .
A: Yeah .
D: " Multi - multi - English " just means " TIMIT " ,
D: right ?
A: Yeah .
B: Yeah .
D: So uh That 's D: Yeah .
D: So . And and when you add other things in to to broaden it , it gets worse uh typically .
A: Mmm .
D: Yeah .
A: Mm - hmm .
A: Then uh some questions without answers .
D: OK .
A: Uh , training set ,
A: um ,
D: Uh - huh .
A: uh , training targets D: I like that .
D: The training set is both questions , with answers and without answers .
A: It 's A: Yeah .
A: Yeah .
D: It 's sort of , yes it 's mul it 's multi - uh - purpose .
A: Yeah .
D: OK .
A: Uh , training
A: s Right . So Yeah , the training targets actually ,
A: the two of the main issues perhaps are still the language dependency and the noise dependency .
A: And perhaps to try to reduce the language dependency , we should focus on finding some other kind of training targets .
D: Mm - hmm .
A: And labeling s labeling seems important
A: uh , because of TIMIT results .
D: Mm - hmm .
A: Uh .
A: For moment you use we use phonetic targets
A: but we could also use articulatory targets , soft targets ,
A: and perhaps even , um use networks that doesn't do classification
A: but just regression
A: so uh , train to have neural networks that
A: um , um , uh ,
D: Mm - hmm .
A: does a regression
A: and well , basically com com compute features and noit not , nnn , features without noise . I mean uh , transform the fea noisy features in other features that are not noisy .
A: But continuous features .
A: Not uh uh , hard targets .
D: Mm - hmm .
D: Mm - hmm .
A: Uh D: Yeah , that seems like a good thing to do , probably ,
D: not uh again a short - term sort of thing .
A: Yeah .
D: I mean one of the things about that is that um it 's D: e u the ri I guess the major risk you have there of being is being dependent on very dependent on the kind of noise and and so forth .
A: Yeah .
A: f But , yeah .
D: Uh . But it 's another thing to try .
A: So , this is w w i wa wa this is one thing , this this could be could help could help perhaps to reduce language dependency
A: and for the noise part um we could combine this with other approaches , like , well , the Kleinschmidt approach .
A: So the d the idea of putting all the noise that we can find inside a database .
D: Mm - hmm .
B: Yeah .
A: I think Kleinschmidt was using more than fifty different noises to train his network ,
A: and So this is one approach
D: Mm - hmm .
A: and the other is multi - band  uh , that I think is more robust to the noisy changes .
D: Mm - hmm .
A: So perhaps , I think something like multi - band trained on a lot of noises with uh , features - based targets could could could help .
D: Yeah , if you i i It 's interesting thought
D: maybe if you just trained up D: I mean w yeah , one one fantasy would be you have something like articulatory targets
D: and you have um some reasonable database ,
D: um
D: but then which is um copied over many times with a range of different noises ,
A: Mm - hmm .
D: And uh  If Cuz what you 're trying to do is come up with a a core , reasonable feature set which is then gonna be used uh , by the the uh HMM system .
A: Mm - hmm .
D: So .
D: Yeah , OK .
A: So ,
A: um , yeah .
A: The future work is , well , try to connect to the to make to plug in the system to the OGI
A: system .
A: Um , there are still open questions there ,
D: Mm - hmm .
A: where to put the MLP basically .
A: Um .
D: And I guess , you know , the the the real open question ,
D: I mean , e u there 's lots of open questions ,
D: but one of the core quote <comment> " open questions " for that is um , um , if we take the uh you know , the best ones here ,
D: maybe not just the best one ,
D: but the best few or something D: You want the most promising group from these other experiments .
D: Um , how well do they do over a range of these different tests , not just the Italian ?
A: Mmm ,
D: Um .
D: And y
D: y Right ?
A: Yeah , yeah .
D: And then um then see , again , how D: We know that there 's a mis there 's a uh a a loss in performance when the neural net is trained on conditions that are different than than , uh we 're gonna test on ,
D: but well , if you look over a range of these different tests um , how well do these different ways of combining the straight features with the MLP features , uh stand up over that range ?
B: Mm - hmm .
D: That 's that that seems like the the the real question .
D: And if you know that D: So if you just take PLP with uh , the double - deltas .
D: Assume that 's the p the feature .
D: look at these different ways of combining it .
D: And uh , take let 's say , just take uh multi - English
D: cause that works pretty well for the training .
A: Mm - hmm .
D: And just look take that case and then look over all the different things .
D: How does that How does that compare between the A: So all the all the test sets you mean ,
A: yeah .
B: Yeah .
D: All the different test sets ,
A: And D: and for and for the couple different ways that you have of of of combining them .
A: Yeah .
D: Um . How well do they stand up , over the B: Mm - hmm .
A: Mmm .
A: And perhaps doing this for cha changing the variance of the streams and so on getting different scaling D: That 's another possibility if you have time ,
D: yeah .
D: Yeah .
A: Um .
A: Yeah , so thi this sh would be more working on the MLP as an additional path instead of an insert to the to their diagram .
A: Cuz A: Yeah .
A: Perhaps the insert idea is kind of strange
A: because nnn , they they make LDA
A: and then we will again add a network does discriminate anal nnn , that discriminates ,
D: Yeah . It 's a little strange
A: or ?
D: but on the other hand they did it before .
A: Mmm ?
D: Um the
A: Mmm .
A: And and and
A: yeah .
A: And because also perhaps we know that the when we have very good features the MLP doesn't help .
A: So . I don't know .
D: Um , the other thing , though , is that
D: um D: So . Uh , we we wanna get their path running here ,
D: right ?
D: If so , we can add this other stuff .
A: Um .
D: as an additional path