Grad H: That 's right .
PhD D: The fa
Grad F: Yeah .
PhD D :
Grad H: " Please throw everything in that direction . "
Professor B: Yeah . Anyway .
Grad H: Padded cell .
Postdoc G: It 'd be fun to get like a a p visit from the Grad H: There was a dorm room at Tech that , uh , someone had coated the walls and the ceiling , and , uh , the floor with mattresses .
Grad F: Mmm .
Grad H: The entire room .
Professor B: I had as my fourth thing here processing of wave forms .
Postdoc G: Yeah .
Professor B: What did we mean by that ? Remember @ @ ?
Grad H: Uh , Liz wanted to talk about methods of improving accuracy by doing pre - processing .
Postdoc G: Pre - processing .
PhD A: Well I think that that was just sort of I I already asked Thilo
Professor B: Oh , you already did that .
PhD A: but that , um , it would be helpful if I can stay in the loop somehow with , um , people who are doing any kind of post - processing , whether it 's to separate speakers or to improve the signal - to - noise ratio , or both , um , that we can sort of try out as we 're running recognition . Um , so , i is that Who else is work I guess Dan Ellis and you
PhD C: Dan , yeah .
Professor B: Yeah , and Dave uh Gel - Gelbart again ,
Grad H: Yep .
PhD A: and Dave .
PhD C: Yep .
PhD A: OK .
PhD D: Yeah .
Professor B: he 's he 's interested in in fact we 're look starting to look at some echo cancellation kind of things .
PhD A: OK .
Professor B: Which uh Grad H: I am not sure how much that 's an issue with the close talking mikes ,
Professor B: Hmm ?
Grad H: but who knows ?
Professor B: Well , let 's w i isn't that what what you want PhD A: I don't know . I 'm bad Professor B: t No , so No , i w wha what you what you want when you 're saying improving the wave form you want the close talking microphone to be better .
PhD A: It 's like {comment} like Professor B: Right ?
Grad H: Right .
Professor B: And the question is to w to what extent is it getting hurt by , uh by any room acoustics or is it just uh , given that it 's close it 's not a problem ?
PhD A: It doesn't seem like big room acoustics problems to my ear
Professor B: Uh PhD A: but I 'm not an expert . It seems like a problem with cross - talk .
Professor B: OK , so it 's PhD C: Yeah .
Grad H: e I bet with the lapel mike there 's plenty , uh , room acoustic
PhD A: That that may be true .
Grad H: but I I think the rest is cross - talk .
PhD A: But I don't know how good it can get either by those the those methods Grad H: Yeah .
PhD A: That 's true .
Professor B: OK .
Grad H: So I I think it 's just ,
PhD A: Oh , I don't know .
Grad H: yeah , what you said , cross - talk .
PhD A: All I meant is just that as sort of as this pipeline of research is going on we 're also experimenting with different ASR , uh , techniques .
Grad H: Mm - hmm .
PhD A: And so it 'd be w good to know about it .
PhD E: So the problem is like , uh , on the microphone of somebody who 's not talking they 're picking up signals from other people {comment} and that 's causing problems ?
PhD A: R right , although if they 're not talking , using the the inhouse transcriptions , were sort of O K because the t no one transcribed any words there and we throw it out .
PhD E: Mm - hmm .
PhD A: But if they 're talking at all and they 're not talking the whole time , so you get some speech and then a " mm - hmm " , and some more speech , so that whole thing is one chunk . And the person in the middle who said only a little bit is picking up the speech around it , that 's where it 's a big problem .
Postdoc G: You know , this does like seem like it would relate to some of what Jose 's been working on as well , the encoding of the PhD D: Yeah .
Postdoc G: And and he also , he was PhD A: The energy ,
PhD D: Yeah ,
PhD A: right . Exactly .
PhD D: energy .
Postdoc G: I was t I was trying to remember , you have this interface where you i you ha you showed us one time on your laptop that you you had different visual displays as speech and nonspeech events .
PhD D: Yeah , c Yeah . May I I only display the different colors for the different situation . But , eh , for me and for my problems , is uh is enough . Because , eh , it 's possible , eh , eh , in a simp sample view , uh , to , nnn , to compare with c with the segment , the the kind of assessment what happened with the the different parameters . And only with a different bands of color for the , uh , few situation , eh , I consider for acoustic event is enough to @ @ .
Postdoc G: Mm - hmm .
PhD D: I I  I see that , eh , you are considering now , eh , a very sophisticated , eh , ehm , eh , @ @ {comment} set of , eh , graphic s eh , eh , ehm , si symbols to to transcribe . No ? Because , uh , before , you you are talking about the the possibility to include in the Transcriber program eh , um , a set of symbols , of graphic symbol to t to mark the different situations during the transcription
Postdoc G: Oh , I w Uh - huh .
PhD D: during the transcription . No ?
Postdoc G: Well , you 're saying So , uh , symbols for differences between laugh , and sigh , and and and slam the door and stuff ?
PhD D: Yeah . Yeah , yeah . The s the symbols , you you talk of before .
Postdoc G: Or some other kind of thing ?
PhD D: No ? To to mark Postdoc G: Well , I wouldn't say symbols so much . The the main change that I that I see in the interface is is just that we 'll be able to more finely c uh , time things .
PhD D: Yeah . Yeah .
Postdoc G: But I I also st there was another aspect of your work that I was thinking about when I was talking to you
PhD A: Hmm .
Postdoc G: which is that it sounded to me , Liz , as though you and , uh , maybe I didn't q understand this , but it sounded to me as though part of the analysis that you 're doing involves taking segments which are of a particular type and putting them together .
PhD D: Yeah .
Postdoc G: And th so if you have like a p a s you know , speech from one speaker , then you cut out the part that 's not that speaker ,
PhD A: Mm - hmm .
Postdoc G: and you combine segments from that same speaker to {comment} and run them through the recognizer . Is that right ?
PhD A: Well we try to find as close of start and end time of as we can to the speech from an individual speaker ,
Postdoc G: Mm - hmm .
PhD A: because then we we 're more guaranteed that the recognizer will for the forced alignment which is just to give us the time boundaries , because from those time boundaries then the plan is to compute prosodic features .
Postdoc G: Mm - hmm .
PhD A: And the sort of more space you have that isn't the thing you 're trying to align the more errors we have . Um , so , you know , that that it would help to have either pre - processing of a signal that creates very good signal - to - noise ratio ,
Postdoc G: Mm - hmm . Cuz i OK .
PhD A: which I don't know how possible this is for the lapel , um , or to have very to have closer , um , time you know , synch times , basically , around the speech that gets transcribed in it , or both . And it 's just sort of a open world right now of exploring that . So I just wanted to see , you know , on the transcribing end from here things look good . Uh , the IBM one is more it 's an open question right now . And then the issue of like global processing of some signal and then , you know , before we chop it up is is yet another way we can improve things in that .
PhD E: What about increasing the flexibility of the alignment ?
Postdoc G: OK .
PhD E: Do you remember that thing that Michael Finka did ?
PhD A: Mm - hmm .
PhD E: that experiment he did a while back ?
PhD A: Right . You can , um The problem is just that the acoustic when the signal - to - noise ratio is too low , um , you you 'll get , a uh an alignment with the wrong duration pattern or it PhD E: Oh , so that 's the problem , is the the signal - to - noise ratio .
PhD A: Yeah . It 's not the fact that you have like I mean , what he did is allow you to have , uh , words that were in another segment move over to the at the edges of of segmentations .
PhD E: Mm - hmm . Or even words inserted that weren't weren't there .
PhD A: Right , things things near the boundaries where if you got your alignment wrong PhD E: Mm - hmm .
PhD A: cuz what they had done there is align and then chop .
PhD E: Mm - hmm .
PhD A: Um , and this problem is a little bit j more global . It 's that there are problems even in inside the alignments , uh , because of the fact that there 's enough acoustic signal there t for the recognizer to to eat , as part of a word . And it tends to do that . S So , uh ,
PhD D: Yeah .
PhD A: but we probably will have to do something like that in addition . Anyway . So , yeah , bottom bottom line is just I wanted to make sure I can be aware of whoever 's working on these signal - processing techniques for , uh , detecting energies ,
PhD D: Yeah .
PhD A: because that that 'll really help us .
Professor B: O K , uh tea has started out there I suggest we c run through our digits and ,
Postdoc G: OK .
Professor B: Uh , So , OK , we 're done .