E: Oh .
B: But if it if you don't if it doesn't help you much at all , um , or to put it another way , if it helps some people a lot more than it helps other people , uh , if their strategies do , then E: Mm - hmm .
E: So is this , uh ?
E: Uh , Guenter was putting a bunch of Wall Street Journal data on our disks .
B: That 's it .
E: So that 's the data that we 'll be running on ?
B: Yeah .
E: I see .
E: OK .
B: Yeah .
B: So we have the data , just not the recognizer .
B: OK .
E: So this test may take quite a while to run , then . May - judging by the amount of data that he was putting on .
B: Uh , well there 's training and test ,
B: right ?
E: I I guess , I 'm not sure .
E: I just B: No , I mean , if it 's like the other things , there 's there 's data for training the H M Ms and and data for testing it .
B: So I wouldn't B: So it it 's E: OK .
E: So there 's B: So , training the recognizer ,
B: but , um
B: Um .
B: But I think it 's trained on clean and B: Is it trained on clean and and test on ?
D: The Wall Street ?
B: Yeah .
A: Apparently , no .
A: It 's training on a range between ten and twenty DB , I think , and testing between five and fifteen .
D: Mm - hmm .
D: Yeah .
A: That 's what I got on B: OK .
D: It 's , uh It 's like a medium medium - mismatch condition , sort of .
B: I see .
A: Yeah ,
A: and So the noise is There is a range of different noises also um which are selected randomly and added randomly , uh , to the files .
A: And there are noises that are different from the noises used on TI - digits .
B: Yeah .
B: Yeah . I mean , I wouldn't imagine that the amount of testing data was that huge .
B: They probably put training uh , almost certain they put training data there too .
B: Maybe not .
B: So .
B: That 's that .
B: Anybody have anything else ?
E: Uh , one one last question on that .
E: When did they estimate that they would have that system available for download ?
D: Um , I guess I guess one some preliminary version is already there .
E: Oh , so there 's w something you can download to just learn ?
D: Yeah , it 's already there .
E: OK ,
D: Yeah .
E: good .
D: But they 're actually parallel - y doing some modifications also , I think .
E: OK .
D: So I guess the f final system will be frozen by middle of , like , one more week maybe .
B: Oh , well that 's pretty soon .
D: Yeah , that 's just one more .
C: Is this their , um , SVM recognizer ?
D: No , it 's just a straightforward HMM .
C: Oh , OK .
B: You know , their their They have a lot of options in their recognizer and and the SVM is one of the things they 've done with it , but it 's not their more standard thing .
C: Uh - huh .
B: For the most part it 's it 's Gaussian mixtures .
C: Oh , OK .
C: Oh , OK .
B: Yeah .
D: It 's just a HMM , Gaussian mixture model .
C: Gaussian mixture HMM .
B: Yeah .
C: OK .
B: Yeah , the SVM thing was an HMM also .
B: It was just a it it it was like a hybrid , like D: Yeah , this is a g
C: Mm - hmm .
D: yeah , this i
D: yeah .
B: what ?
B: Yeah .
E: So , just so that I understand , they 're providing scripts and everything so that basically , uh , you you push a button and it does training , and then it does test , and everything ?
E: Is that the idea ?
D: I I I think yeah , I I guess something like that .
E: Mm - hmm .
D: It 's like  as painless as possible ,
D: is what Do they provide all the scripts , everything , and then Just ,
E: I see .
E: Hmm .
E: Somehow yo there 's hooks to put your features in and D: ju Yeah , I th I think .
E: Hmm .
B: Hmm .
B: Yeah , um .
B: In fact , I mean , if you look into it a little bit , it might be reasonable B: You know Joe ,
B: right ?
E: Mm - hmm .
B: Yeah .
B: Just to sort of ask him about the issue of , um , different features having different kinds of , uh , scaling characteristics and so on .
B: So that , you know , w w possibly having entirely different optimal values for for the usual twiddle factors
B: and what 's what 's the plan about that ?
E: OK .
D: So sh shall we , like , add Chuck also to the mailing lists ?
D: It may be better , I mean , in that case if he 's going to B: Yeah .
D: Because there 's a mailing list for this .
B: Is that OK ?
E: Yeah ,
E: that 'd be great .
D: Yeah , I guess maybe Hari or Hynek , one of them , has to send a mail to Joe .
D: Or maybe if you E: I I could send him an email .
D: Well , yeah , to add or maybe wh
E: I I know him really well .
E: I I was just talking with him on email the other day actually .
D: Yeah , so that 's just fine .
D: So D: So B: Uh , yeah , and just , um , se maybe see .
E: About other things ,
E: but .
B: Do you have Hari 's , uh ?
E: I have Hari 's B: Yeah , so maybe just CC Hari and say that you 've just been asked to handle the large vocabulary part here ,
E: OK .
B: and , uh , you know ,
E: Would it be better if I asked Hari to ask Joe ?
B: Uh .
B: Why don't you just ask Joe but CC Hari , and then in the note say , " Hari , hopefully this is OK with you " .
E: OK .
E: OK .
B: And then if Joe feels like he needs a confirmation , Hari can answer it .
D: Yeah .
B: That way you can get started asking <comment> Joe quickly while he 's while he 's maybe still , you know , putting in nails and screws and
B: Yeah .
D: And there is an , uh , archive of all the mails that has been gon that has gone , uh , between these people among these people .
D: So just you can see all this mails in the ISIP web site E: OK .
E: OK .
D: Mississippi web site .
E: Is that a password controlled ?
D: Yeah , it 's password protected .
E: OK .
D: So , like like , it 's , like B: Have you thought about how long would be uh , most useful for you to go up to OGI ?
A: I don't know ,
A: uh .
A: We can  For September , we can set up a work schedule and we can maybe work independently .
A: And then at some point it maybe be better to work together again .
B: Oh , so you 're you 're imagining more that you would come back here first for a while and then and then go up there ?
A: I B: I mean , it 's to you .
A: Maybe , yeah .
B: I ju you guys are
B: Well , y anyway , you don't have to decide this second but thi think about it about what what you would think would be the the best way to work it . I 'll
A: But , uh Huh .
A: Mm - hmm .
B: support it either way , so .
A: Mm - hmm
A: Right .
B: OK .
B: Uh .
B: Got anything to tell us ?
C: Um .
C: Well , I 've been reading some literature about clustering of data .
C: Just , um , I guess , let me put it in context .
C: OK , so we 're talking about discovering intermediate categories to , um to classify .
C: And , uh , I was looking at some of the work that , uh , Sangita was doing on these TRAPS things .
C: So she has , um she has temporal patterns for , um , a certain set of phonemes , from from TIMIT ,
C: right ?
C: the most common phonemes .
C: And each one of them has has a a nice pattern over time , a one one second window .
C: And it has has these patterns .
C: Um , so she has , um a TRAP for each one of the phonemes , um , times fifteen , for each of the fifteen critical bands .
C: And , um , she does this agglomerative hierarchical clustering which which basically , um , is a clustering algorithm that , uh , starts with many , many , many different points many different clusters uh , corresponding to the number of data , uh , patterns that you have in the data .
C: And then you have this distance mej metric which , uh , measures how how closely related they are .
C: And you start , um by merging the patterns that are most closely related .
E: And you create a tree .
C: And
C: y yeah , yeah ,
C: a dendrogram tree .
E: Mm - hmm .
C: Um .
E: And then you can pick , uh , values anywhere along that tree to fix your set of clusters .
C: Right , usually it 's when , um when the sol similarity measures , um , don't go down as much .
E: Mm - hmm .
C: And so , uh so you stop at that point .
C: And what she found was , sh um , was there were five broad , um broad categories , uh , corresponding to , uh , things like , uh , fricatives and , uh , vocalic , um , and , uh , stops .
B: Mm - hmm .
C: And , uh , one for silence and and another one for schwa schwa sounds .
C: Um , and , um , I was thinking about ways to to generalize this
C: because w you 're it 's sort of like a it 's not a completely automatic way of clustering ,
C: because yo beforehand you have these these TRAPS and you 're saying that that these frames correspond to this particular phoneme .
C: Um , and that 's that 's constraining your your clustering to to the set of phonemes that you already have .
C: Um , whereas maybe we want to just take take a look at , um , arbitrary windows in time , um , of varying length , um , and cluster those .
E: Mm - hmm .
E: Mm - hmm .
C: And I 'm thinking if we if we do that , then we would probably , um , at some point in the clustering algorithm find that we 've clustered things like , OK , thi this is a transition , um , this is a relatively stable stable point .
C: Um , and I 'm hoping to find other things of of similarity and maybe use these things as the intermediate , um intermediate categories that , uh , um , I 'll later classify .