E: What is the the name in English ?
E: the the , mmm ,  the de detector of , ehm of a word in the in the s in an isolated word in in the background That , uh
H: I 'm I 'm not sure what you 're saying ,
H: can you try E: I mean that when when you use , eh eh any
A: I think he 's saying the onset detector .
E: Yeah .
H: Onset detector , OK .
E: I I think it 's probably to work well eh , because , eh you have eh , in the mixed files a great level of energy .
E: eh and great difference between the sp speaker .
E: And probably is not so easy when you use the the PDA , eh that Because the signal is , eh the in the e energy level .
H: Right .
E: in in that , eh eh speech file is , eh more similar .
H: Right .
E: between the different eh , speaker , um I I think is eh , it will i is my opinion .
H: But different speakers .
E: It will be , eh more difficult to to detect bass - tone energy .
E: the the change .
E: I think that , um
H: Ah , in the clo in the P D A , you mean ?
E: In the PDA .
H: Absolutely .
H: Yeah , no question .
E: Yeah .
E: Yeah .
H: It 'll be much harder .
H: Much harder .
D: Yeah .
E: And the the another question , that when I review the the the work of Javier .
E: I think the , nnn , the , nnn , that the idea of using a neural network to to get a broad class of phonetic , eh from , eh uh a candidate from the the the speech signal .
E: If you have , eh uh , I 'm considering , only because Javier , eh only consider , eh like candidate , the , nnn , eh the silence , because it is the the only model , eh eh , he used that , eh  eh nnn , to detect the the possibility of a a change between the between the speaker ,
H: Right .
E: Um another another research thing , different groups , eh working , eh on Broadcast News prefer to , eh to consider hypothesis eh between each phoneme .
H: Mm - hmm .
H: Yeah , when a phone changes .
E: Because , I I I think it 's more realistic that , uh only consider the  the the the silence between the speaker .
E: Eh there there exists eh silence between between , eh a speaker .
E: is is , eh eh acoustic , eh event , important to to consider .
D: Mm - hmm .
D: Mm - hmm .
E: I I found that the , eh silence in in many occasions in the in the speech file ,
E: but , eh when you have , eh eh , two speakers together without enough silence between between them , eh  I think eh is better to use the acoustic change detector basically and I I I IX or , mmm , BIC criterion for consider all the frames in my opinion .
D: Mm - hmm .
D: Yeah , the you know , the reason that he , uh just used silence was not because he thought it was better , it was it was it was the place he was starting .
E: Yeah .
H: Yep .
E: Yeah .
D: So , he was trying to get something going ,
E: Yeah , yeah , yeah , yeah .
D: and , uh e e you know , as as  as is in your case , if you 're here for only a modest number of months you try to pick a realistic goal ,
E: Yeah .
H: Do something .
E: Yeah , yeah , yeah , yeah .
D: But his his goal was always to proceed from there to then allow broad category change also .
E: Uh - huh .
E: But , eh do do you think that if you consider all the frames to apply the the , eh the BIC criterion to detect the the the different acoustic change , eh between speaker ,
E: without , uh with , uh silence or with overlapping ,
E: uh ,
E: I think like like , eh eh a general , eh eh way of process the the acoustic change .
D: Mm - hmm .
E: In a first step , I mean .
D: Mm - hmm .
E: An - and then , eh  eh without considering the you you you , um you can consider the energy like a another parameter in the in the feature vector ,
H: Right .
H: Absolutely .
E: eh .
D: Mm - hmm .
E: This this is the idea .
E: And if , if you do that , eh eh , with a BIC uh criterion for example , or with another kind of , eh of distance in a first step , and then you , eh you get the , eh the hypothesis to the this change acoustic , eh  to po process
H: Right .
E: Because , eh eh , probably you you can find the the eh a small gap of silence between speaker with eh eh a ga mmm ,  small duration Less than , eh two hundred milliseconds for example
D: Mm - hmm .
E: and apply another another algorithm , another approach like , eh eh detector of ene , eh detector of bass - tone energy to to consider that , eh that , eh zone . of s a small silence between speaker ,
E: or another algorithm to to process , eh the the segment between marks eh founded by the the the BIC criterion and applied for for each frame .
D: Mm - hmm .
E: I think is , eh nnn , it will be a an an a more general approach the if we compare with use , eh a neural net or another , eh speech recognizer with a broad class or or narrow class ,
D: Mm - hmm .
E: because , in my opinion eh it 's in my opinion , eh if you if you change the condition of the speech , I mean , if you adjust to your algorithm with a mixed speech file and to , eh to , eh  adapt the neural net , eh used by Javier with a mixed file .
D: Mm - hmm .
D: Mm - hmm .
H: With the what file ?
E: uh With a m mixed file ,
E: with a the mix , mix .
F: " Mixed . "
A: " Mixed " .
H: " Mixed ? "
D: Mm - hmm .
E: Sorry .
E: And and then you you , eh you try to to apply that , eh , eh , eh , speech recognizer to that signal , to the PDA , eh speech file , I I think you will have problems , because the the the the condition you you will need t t I I suppose that you will need to to to retrain it .
H: Oh , absolutely .
D: Well , I I H: This is this is not what I was suggesting to do .
D: u Look , I I think this is a E: Really ?
D: One once It 's a I used to work , like , on voiced on voice silence detection , you know , and this is this kind of thing .
E: Yeah .
D: Um If you have somebody who has some experience with this sort of thing , and they work on it for a couple months , they can come up with something that gets most of the cases fairly easily .
D: Then you say , " OK , I don't just wanna get most of the cases I want it to be really accurate . "
D: Then it gets really hard no matter what you do .
D: So , the p the problem is is that if you say , " Well I I have these other data over here , that I learn things from , either explicit training of neural nets or of Gaussian mixture models or whatever . "
E: Yeah .
D: Uh Suppose you don't use any of those things .
D: You say you have looked for acoustic change .
D: Well , what does that mean ?
D: That that means you set some thresholds somewhere or something ,
E: Yeah .
D: right ?
E: Yeah .
D: and and so where do you get your thresholds from ?
D: From something that you looked at .
D: So you always have this problem , you 're going to new data
D: um H how are you going to adapt whatever you can very quickly learn about the new data ? Uh , if it 's gonna be different from old data that you have ?
D: And I think that 's a problem with this .
H: Well , also what I 'm doing right now is not intended to be an acoustic change detector for far - field mikes .
H: What I 'm doing is trying to use the close - talking mike and just use  Can - and just generate candidate and just try to get a first pass at something that sort of works .
E: Yeah !
G: Actually actually actually A: You have candidates .
E: the candidate .
G: I A: to make marking easier .
G: Or A: Yeah .
H: and I haven't spent a lot of time on it and I 'm not intending to spend a lot of time on it .
G: OK .
G: I um , I , unfortunately , have to run ,
H: So .
G: but , um I can imagine uh building a um model of speaker change detection that takes into account both the far - field and the uh actually , not just the close - talking mike for that speaker , but actually for all of th for all of the speakers .
H: Yep .
H: Everyone else .
D: Yeah .
G: um If you model the  the effect that me speaking has on your microphone and everybody else 's microphone , as well as on that , and you build , um