A: But you could I mean you can you can align so that It 's not perfect ,
A: but if you if you know what was said
A: and B: But the problem is that their models are all word level models .
B: So there 's no phone models that you get alignments for .
F: Mm - hmm .
A: Oh .
B: You So you could find out where the word boundaries are
B: but that 's about it .
A: Yeah .
A: I see .
E: S But we could use uh the the noisy version that TIMIT ,
E: which you know , is similar to the the noises found in the TI - digits um portion of Aurora .
F: Yeah .
F: noise , yeah .
F: Yeah , that 's right , yep .
F: Mmm .
F: Well , I guess I guess we can we can say that it will help ,
A: Yeah .
F: but I don't know .
F: If this voicing bit doesn't help , uh , I think we don't have to to work more about this
F: because F: Uh . It 's just to know if it how much i it will help
A: Uh .
A: Yeah .
F: and to have an idea of how much we can gain .
A: Right .
A: I mean in experiments that we did a long time ago
F: Mmm .
A: and different ta
A: it was probably Resource Management or something ,
A: um , I think you were getting something like still eight or nine percent error on the voicing , as I recall .
A: And um , so um
E: Another person 's voice .
A: what that said is that , sort of , left to its own devices , like without the a strong language model and so forth , that you would  you would make significant number of errors just with your uh probabilistic machinery in deciding
B: It also A: one oh
B: Yeah , the though I think uh there was one problem with that in that , you know , we used canonical mapping
B: so our truth may not have really been true to the acoustics .
A: Uh - huh .
E: Hmm .
B: So .
F: Mmm .
A: Yeah .
A: Well back twenty years ago when I did this voiced - unvoiced stuff , we were getting more like ninety - seven or ninety - eight percent correct in voicing .
A: But that was speaker - dependent actually .
A: We were doing training on a particular announcer
F: Mm - hmm .
A: and and getting a very good handle on the features .
F: Mm - hmm .
A: And we did this complex feature selection thing where we looked at all the different possible features one could have for voicing
A: and  and and uh and exhaustively searched all size subsets
B: Wow !
A: and and uh for for that particular speaker and you 'd find you know the five or six features which really did well on them .
F: Mm - hmm .
A: And then doing doing all of that we could get down to two or three percent error .
F: Mm - hmm .
A: But that , again , was speaker - dependent with lots of feature selection
A: and a very complex sort of thing .
F: Mmm .
A: So I would I would believe that uh it was quite likely that
A: um
A: looking at envelope only , that we 'd be significantly worse than that .
F: Mm - hmm .
A: Uh .
F: And the all the the SpeechCorders ?
F: what 's the idea behind ?
F: Cuz they they have to F: Oh , they don't even have to detect voiced spe speech ?
A: The modern ones don't do a  a simple switch .
F: They just work on the code book
F: and find out the best excitation .
A: They work on the code book excitation .
A: Yeah they do analysis - by - synthesis .
A: They try they they try every every possible excitation they have in their code book and find the one that matches best .
F: Yeah .
F: Mmm .
F: Alright .
F: Yeah .
F: So it would not help .
A: Yeah .
E: Hmm .
A: Uh .
A: O K .
B: Can I just mention one other interesting thing ?
A: Yeah .
B: Um . One of the ideas that we had come up with last week for things to try to improve the system B: Um .
B: Actually I I s we didn't I guess I wrote this in after the meeting
B: b but the thought I had was um looking at the language model that 's used in the HTK recognizer ,
B: which is basically just a big loop ,
E: Mm - hmm .
B: right ?
B: So you it goes " digit "
D: Mm - hmm .
B: and then that can be either go to silence or go to another digit ,
B: which B: That model would allow for the production of infinitely long sequences of digits ,
B: right ?
A: Right .
B: So . I thought " well I 'm gonna just look at the what actual digit strings do occur in the training data . "
A: Right .
B: And the interesting thing was it turns out that there are no sequences of two - long or three - long digit strings in any of the Aurora training data .
B: So it 's either one , four , five , six ,
B: uh up to eleven ,
B: and then it skips
B: and then there 's some at sixteen .
A: But what about the testing data ?
B: Um . I don't know .
B: I didn't look at the test data yet .
A: Yeah .
B: So .
A: I mean if there 's some testing data that has has  has two or three B: Yeah .
B: But I just thought that was a little odd ,
B: that there were no two or three long B: Sorry .
B: So I I just for the heck of it , I made a little grammar which um , you know , had it 's separate path for each length digit string you could get .
B: So there was a one - long path
B: and there was a four - long and a five - long
A: Mm - hmm .
B: and I tried that and it got way worse .
B: There were lots of deletions .
B: So it was  you know , I I didn't have any weights of these paths
A: Mm - hmm .
B: or I didn't have anything like that .
A: Mm - hmm .
B: And I played with tweaking the word transition penalties a bunch ,
B: but I couldn't go anywhere .
B: But um .
A: Hmm .
B: I thought " well if I only allow "
B: Yeah , I guess I should have looked at to see how often there was a mistake where a two - long or a three - long path was actually put out as a hypothesis .
B: Um . But .
A: Hmm .
B: So to do that right you 'd probably want to have  allow for them all
B: but then have weightings and things .
B: So . I just thought that was a interesting thing about the data .
A: OK .
A: So we 're gonna read some more digit strings I guess ?
B: Yeah .
B: You want to go ahead , Morgan ?
A: Sure .