Professor B: Yeah . Yeah . The same sort of numbers that we got those graphs from . Right ?
Grad E: Could we do exactly the same thing that we 're doing now , but do it with a far - field mike ?
Professor B: Yeah , do it with one of on
Grad E: Cuz we extract the times from the near - field mike , but you use the acoustics from the far - field mike .
PhD A: Right . I understand that . I just meant that so you have three choices . There 's , um You can use times where that person is talking only from the transcripts but the segmentations were were synchronized . Or you can do a forced alignment on the close - talking to determine that , the you know , within this segment , these really were the times that this person was talking and elsewhere in the segment other people are overlapping and just front - end those pieces . Or you can run it on the whole data , which is which is , you know , a Professor B: But but but how did we get the how did we determine the links , uh , that we 're testing on in the stuff we reported ?
PhD A: In the H L T paper we took segments that are channel time - aligned , which is now h being changed in the transcription process , which is good , and we took cases where the transcribers said there was only one person talking here , because no one else had time any words in that segment and called that " non - overlap " .
Professor B: And tha And that 's what we were getting those numbers from .
PhD A: Yes . Tho - good the good numbers .
Professor B: Right .
PhD A: The bad numbers were from the segments where there was overlap .
Professor B: Well , we could start with the good ones .
PhD A: Yeah .
Professor B: But anyway so I think that we should try it once with the same conditions that were used to create those , and in those same segments just use one of the P Z
PhD A: Right . So we we can do that . Yeah .
Professor B: And then , you know , I mean , the thing is if we were getting , uh what , thirty - five , forty percent , something like that on on that particular set , uh , does it go to seventy or eighty ?
PhD A: Right .
Professor B: Or , does it use up so much memory we can't decode it ?
PhD A: It might also depend on which speaker th it is and how close they are to the PZM ?
Professor B: Uh PhD A: I don't know how different they are from each other .
PhD F: You want to probably choose the PZM channel that is closest to the speaker .
PhD A: To be best PhD D: Yeah .
Grad E: For this particular digit ones , I just picked that one .
PhD A: f
Professor B: Well PhD A: OK . So we would then use that one , too ,
Grad E: So PhD F: Oh , OK .
Professor B: This is kind of central .
PhD A: or ?
Professor B: You know , it 's so i but I would I 'd pick that one . It 'll be less good for some people than for other , but I I 'd like to see it on the same exact same data set that that we did the other thing on .
Grad E: Actually I sh actually should 've picked a different one ,
Professor B: Right ?
Grad E: because that could be why the PDA is worse . Because it 's further away from most of the people reading digits .
PhD D: It 's further away . Yeah . Yeah .
Professor B: That 's probably one of the reasons .
Postdoc C: Hmm . Mm - hmm .
PhD A: Well , yeah . You could look at , I guess , that PZM or something .
Grad E: Yep .
Professor B: But the other is , it 's very , uh I mean , even though there 's I 'm sure the f f the the SRI , uh , front - end has some kind of pre - emphasis , it 's it 's , uh  still , th it 's picking up lots of low - frequency energy .
PhD F: Mm - hmm .
Professor B: So , even discriminating against it , I 'm sure some of it 's getting through . Um . But , yeah , you 're right . Prob - a part of it is just the distance .
PhD A: And aren't these pretty bad microphones ?
Grad E: Yep .
PhD A: I mean Professor B: Well , they 're bad . But , I mean , if you listen to it , it sounds OK . You know ? u Yeah .
Grad E: Yeah . When you listen to it , uh , the PZM and the PDA Yeah , th the PDA has higher sound floor but not by a lot . It 's really pretty uh , pretty much the same .
PhD A: I just remember you saying you got them to be cheap on purpose . Cheap in terms of their quality . So .
Professor B: Well , they 're twenty - five cents or so .
Grad E: Th - we wanted them to be to be typical of what would be in a PDA .
Professor B: Yeah .
PhD A: Mm - hmm .
Grad E: So they are they 're not the PZM three hundred dollar type . They 're the twenty - five cent ,
Professor B: Yeah .
Grad E: buy them in packs of thousand type .
PhD A: I see .
Professor B: But , I mean , the thing is people use those little mikes for everything because they 're really not bad .
Grad E: Everything .
PhD A: Mm - hmm .
Professor B: I mean , if you 're not doing something ridiculous like feeding it to a speech recognizer , they they  they you know , you can hear the sou hear the sounds just fine .
PhD A: Right .
Professor B: You know , it 's They I mean , i it 's more or less the same principles as these other mikes are built under , it 's just that there 's less quality control . They just , you know , churn them out and don't check them . Um . So . So that was Yeah . So that was i interesting result . So like I said , the front - end guys are very much interested in in this is as as well and
PhD F: So so , but where is this now ? I mean , what 's where do we go from here ?
Grad E: Yeah . That was gonna be my question .
PhD F: I mean , we so we have a we have a a system that works pretty well but it 's not , you know , the system that people here are used to using to working with .
Professor B: Well , I think what we wanna do is we want to eh ,
PhD F: So what what do we do now ?
Professor B: and we 've talked about this in other contexts we want to have the ability to feed it different features .
PhD F: Mm - hmm .
Professor B: And then , um , from the point of view of the front - end research , it would be s uh , substituting for HTK .
PhD F: OK . OK .
Professor B: I think that 's the key thing . And then if we can feed it different features , then we can try all the different things that we 're trying there .
PhD F: OK . Alright .
Professor B: And then , um , uh , also Dave is is thinking about using the data in different ways , uh , to um , uh , explicitly work on reverberation
PhD F: Mm - hmm .
Professor B: starting with some techniques that some other people have found somewhat useful , and Yeah .
PhD F: OK . So so the key thing that 's missing here is basically the ability to feed , you know , other features i into the recognizer
Professor B: Right .
PhD F: and also then to train the system .
Professor B: Right .
PhD F: OK . And , uh , es I don't know when Chuck will be back but that 's exactly what he he 's gonna Professor B: H h He 's he 's sort of back , but he drove for fourteen hours an and wasn't gonna make it in today .
PhD F: Oh , OK . So , I think that 's one of the things that he said he would be working on . Um .
Grad E: Yeah .
PhD F: Just sort of t to make sure that we can do that
Professor B: Yeah .
PhD F: and Um .
Professor B: Right .
PhD F: It 's uh , I mean , the the front - end is f i tha that 's in the SRI recognizer is very nice in that it does a lot of things on the fly but it unfortunately is not designed and , um  like the , uh , ICSI system is , where you can feed it from a pipeline of of the command . So , the what that means probably for the foreseeable future is that you have to , uh , dump out , um you know , if you want to use some new features , you have to dump them into individual files and give those files to the recognizer .
Grad E: We do we tend to do that anyway .
PhD F: OK .
Grad E: Oh . So , although you you can pipe it as well , we tend to do it that way because that way you can concentrate on one block and not keep re - doing it over and over .
PhD F: Oh , OK .
Professor B: Yeah .
PhD F: Alright .
Professor B: Yeah . So I 've I Grad E: So tha that 's exactly what the P - file is for .
Professor B: Yeah .
PhD F: Yeah , the the the cumbersome thing is is , um is that you actually have to dump out little little files .
PhD A: Uh PhD F: So for each segment that you want to recognize you have to dump out a separate file .
Grad E: Uh - huh .
PhD F: Just like i th like th as if there were these waveform segments , but instead you have sort of feature file segments . But , you know So .
Professor B: Cool . OK . So the s the the next thing we had on the agenda was something about alignments ?
PhD A: Oh . Yes , we have I don't know , did you wanna talk about it , or ? I can give a I was just telling this to Jane and and W we we were able to get some definite improvement on the forced alignments by looking at them first and then realizing the kinds of errors that were occurring and um , some of the errors occurring very frequently are just things like the first word being moved to as early as possible in the recognition , which is a um , I think was both a a pruning problem and possibly a problem with needing constraints on word locations . And so we tried both of these st things . We tried saying I don't know , I got this whacky idea that just from looking at the data , that when people talk their words are usually chunked together . It 's not that they say one word and then there 's a bunch of words together . They 're {comment} might say one word and then another word far away if they were doing just backchannels ? But in general , if there 's , like , five or six words and one word 's far away from it , that 's probably wrong on average . So , um And then also , ca the pruning , of course , was too too severe .
PhD F: So that 's actually interesting . The pruning was the same value that we used for recognition . And we had lowered that we had used tighter pruning after Liz ran some experiments showing that , you know , it runs slower and there 's no real difference in PhD A: Actually it was better with slightly better or about th
Grad E: No gain .
PhD A: it was the same with tighter pruning .
PhD F: Right . So for free recognition , this the lower pruning value is better .
PhD A: It 's probably cuz the recognition 's just bad en at a point where it 's bad enough that that you don't lose anything .
PhD F: You Correct . Right . Um , but it turned out for for to get accurate alignments it was really important to open up the pruning significantly .
PhD A: Right .
Professor B: Hmm .
PhD F: Um because otherwise it would sort of do greedy alignment , um , in regions where there was no real speech yet from the foreground speaker .
Professor B: Mm - hmm .
PhD F: Um , so that was one big factor that helped improve things and then the other thing was that , you know , as Liz said the we f enforce the fact that , uh , the foreground speech has to be continuous . It cannot be you cannot have a background speech hypothesis in the middle of the foreground speech . You can only have background speech at the beginning and the end .
PhD A: Yeah . I mean , yeah , it isn't always true , and I think what we really want is some clever way to do this , where , um , you know , from the data or from maybe some hand - corrected alignments from transcribers that things like words that do occur just by themselves a alone , like backchannels or something that we did allow to have background speech around it PhD D: Yeah .
PhD A: those would be able to do that ,
Postdoc C: Sorry .