Meeting transcript:
speaker: C
text: Uh , is it the twenty - fourth ?
speaker: F
text: now we 're on .
speaker: C
text: Yeah .
speaker: A
text: Uh Chuck , is the mike type wireless <disfmarker>
speaker: F
text: Yes .
speaker: A
text: wireless headset ?
speaker: F
text: Yes .
speaker: A
text: OK .
speaker: C
text: Yeah .
speaker: F
text: For you it is .
speaker: C
text: Yeah .
speaker: C
text: We uh <disfmarker> we abandoned the lapel
speaker: C
text: because they sort of were not too <disfmarker> not too hot , not too cold ,
speaker: C
text: they were <disfmarker> you know , they were <vocalsound> uh , far enough away that you got more background noise , uh , and uh <disfmarker> and so forth
speaker: A
text: Uh - huh .
speaker: C
text: but they weren't so close that they got quite the <disfmarker> you know , the really good <disfmarker> No , th
speaker: A
text: OK .
speaker: C
text: they <disfmarker> I mean they didn't <disfmarker>
speaker: C
text: Wait a minute .
speaker: C
text: I 'm saying that wrong .
speaker: C
text: They were not so far away that they were really good representative distant mikes ,
speaker: C
text: but on the other hand they were not so close that they got rid of all the interference .
speaker: A
text: Uh - huh .
speaker: C
text: So it was no <disfmarker> didn't seem to be a good point to them .
speaker: C
text: On the other hand if you only had to have one mike in some ways you could argue the lapel was a good choice ,
speaker: C
text: precisely because it 's in the middle .
speaker: A
text: Yeah , yeah .
speaker: C
text: There 's uh , some kinds of junk that you get with these things that you don't get with the lapel
speaker: C
text: uh , little mouth clicks and breaths and so forth are worse with these than with the lapel ,
speaker: C
text: but
speaker: C
text: given the choice we <disfmarker> there seemed to be very strong opinions for uh , getting rid of lapels .
speaker: C
text: So ,
speaker: A
text: The mike number is <disfmarker>
speaker: F
text: Uh , your mike number 's written on the back of that unit there .
speaker: A
text: Oh yeah .
speaker: A
text: One .
speaker: F
text: And then the channel number 's usually one less than that .
speaker: A
text: Oh , OK .
speaker: F
text: It - it 's one less than what 's written on the back of your <disfmarker>
speaker: A
text: OK .
speaker: A
text: OK .
speaker: A
text: OK .
speaker: F
text: yeah .
speaker: F
text: So you should be zero , actually .
speaker: A
text: Hello ?
speaker: A
text: Yeah .
speaker: F
text: For your uh , channel number .
speaker: A
text: Yep , yep .
speaker: C
text: And you should do a lot of talking so we get a lot more of your pronunciations .
speaker: C
text: no , they don't <disfmarker> don't have a <disfmarker> have any Indian pronunciations .
speaker: F
text: So what we usually do is um , we typically will have our meetings
speaker: C
text: Yeah .
speaker: F
text: and then at the end of the meetings we 'll read the digits .
speaker: F
text: Everybody goes around and reads the digits on the <disfmarker> the bottom of their forms .
speaker: C
text: Session R
speaker: D
text: R - nineteen ?
speaker: A
text: OK .
speaker: C
text: R - nineteen .
speaker: F
text: Yeah .
speaker: F
text: We 're <disfmarker> This is session R - nineteen .
speaker: C
text: If you say so .
speaker: C
text: O K .
speaker: C
text: Do we have anything like an agenda ?
speaker: C
text: What 's going on ?
speaker: C
text: Um .
speaker: C
text: I guess
speaker: C
text: um .
speaker: C
text: So .
speaker: F
text: Sunil 's here for the summer ?
speaker: C
text: One thing <disfmarker>
speaker: C
text: Sunil 's here for the summer ,
speaker: C
text: right .
speaker: C
text: Um ,
speaker: C
text: so , one thing is to talk about a kick off meeting
speaker: C
text: maybe
speaker: C
text: uh ,
speaker: C
text: and then just uh , I guess uh , progress reports individually , and then uh , plans for where we go between now and then , pretty much .
speaker: C
text: Um .
speaker: F
text: I could say a few words about um , some of the uh , compute stuff that 's happening around here ,
speaker: F
text: so that people in the group know .
speaker: C
text: Mm - hmm .
speaker: C
text: OK .
speaker: C
text: Why don't you start with that ?
speaker: C
text: That 's sort of <disfmarker>
speaker: F
text: OK .
speaker: F
text: We um <disfmarker>
speaker: C
text: Yeah ?
speaker: F
text: So we just put in an order for about twelve new machines , uh , to use as sort of a compute farm .
speaker: F
text: And um ,
speaker: F
text: uh , we ordered uh , SUN - Blade - one - hundreds ,
speaker: F
text: and um ,
speaker: F
text: I 'm not sure exactly how long it 'll take for those to come in ,
speaker: F
text: but , uh , in addition , we 're running <disfmarker>
speaker: F
text: So the plan for using these is , uh , we 're running P - make and Customs here
speaker: F
text: and Andreas has sort of gotten that all uh , fixed up and up to speed .
speaker: F
text: And he 's got a number of little utilities that make it very easy to um , <vocalsound> run things using P - make and Customs .
speaker: F
text: You don't actually have to write P - make scripts and things like that .
speaker: F
text: The simplest thing <disfmarker>
speaker: F
text: And I can send an email around
speaker: F
text: or , maybe I should do an FAQ on the web site about it or something .
speaker: F
text: Um ,
speaker: F
text: there 's a c
speaker: C
text: How about an email that points to the FAQ ,
speaker: F
text: Yeah , yeah .
speaker: C
text: you know what I 'm saying ?
speaker: C
text: so that you can <disfmarker>
speaker: C
text: Yeah .
speaker: F
text: Uh , there 's a command , uh , that you can use called " run command " .
speaker: F
text: " Run dash command " ,
speaker: F
text: " run hyphen command " .
speaker: F
text: And , if you say that and then some job that you want to execute , uh , it will find the fastest currently available machine , and export your job to that machine ,
speaker: F
text: and uh <disfmarker> and run it there
speaker: F
text: and it 'll duplicate your environment .
speaker: F
text: So
speaker: F
text: you can try this as a simple test with uh , the L S command .
speaker: F
text: So you can say " run dash command L S " ,
speaker: F
text: and , um , it 'll actually export that <vocalsound> LS command to some machine in the institute , and um , do an LS on your current directory .
speaker: F
text: So , substitute LS for whatever command you want to run ,
speaker: F
text: and um <disfmarker> And that 's a simple way to get started using <disfmarker> using this .
speaker: F
text: And , so , soon , when we get all the new machines up , <vocalsound> um , e then we 'll have lots more compute to use .
speaker: F
text: Now th one of the nice things is that uh , each machine that 's part of the P - make and Customs network has attributes associated with it .
speaker: F
text: Uh , attributes like how much memory the machine has ,
speaker: F
text: what its speed is ,
speaker: F
text: what its operating system ,
speaker: F
text: and when you use something like " run command " , you can specify those attributes for your program .
speaker: F
text: For example if you only want your thing to run under Linux , you can give it the Linux attribute ,
speaker: F
text: and then it will find the fastest available Linux machine and run it on that .
speaker: F
text: So .
speaker: F
text: You can control where your jobs go , to a certain extent ,
speaker: F
text: all the way down to an individual machine .
speaker: F
text: Each machine has an attribute which is the name of itself .
speaker: F
text: So you can give that as an attribute and it 'll only run on that .
speaker: F
text: If there 's already a job running , on some machine that you 're trying to select , your job will get queued up ,
speaker: F
text: and then when that resource , that machine becomes available , your job will get exported there .
speaker: F
text: So ,
speaker: F
text: there 's a lot of nice features to it
speaker: F
text: and it kinda helps to balance the load of the machines
speaker: F
text: and uh ,
speaker: F
text: right now Andreas and I have been the main ones using it
speaker: F
text: and we 're <disfmarker> Uh .
speaker: F
text: The SRI recognizer has all this P - make customs stuff built into it .
speaker: F
text: So .
speaker: C
text: So as I understand , you know , he 's using all the machines and you 're using all the machines ,
speaker: F
text: Yeah .
speaker: C
text: is the rough division of <disfmarker>
speaker: F
text: Exactly .
speaker: F
text: Yeah , you know , I <disfmarker> I sort of got started <comment> using the recognizer just recently
speaker: F
text: and uh ,
speaker: F
text: uh I fired off a training job ,
speaker: F
text: and then I fired off a recognition job
speaker: F
text: and I get this email about midnight from Andreas saying , " uh , are you running two <vocalsound> trainings simultaneously
speaker: F
text: s my m my jobs are not getting run . "
speaker: F
text: So I had to back off a little bit .
speaker: F
text: But ,
speaker: F
text: soon as we get some more machines then uh <disfmarker> then we 'll have more compute available .
speaker: F
text: So ,
speaker: F
text: um ,
speaker: F
text: that 's just a quick update about what we 've got .
speaker: G
text: Um , I have <disfmarker> I have a question about the uh , parallelization ?
speaker: F
text: So .
speaker: F
text: Mm - hmm .
speaker: G
text: So , um , let 's say I have like , a thousand little <disfmarker> little jobs to do ?
speaker: F
text: Mm - hmm .
speaker: G
text: Um , how do I do it with " run command " ?
speaker: G
text: I mean do <disfmarker>
speaker: F
text: You could write a script uh , which called run command on each sub - job
speaker: G
text: Uh - huh .
speaker: G
text: A thousand times ?
speaker: F
text: right ?
speaker: G
text: OK .
speaker: F
text: But you probably wanna be careful with that
speaker: F
text: because um , you don't wanna saturate the network .
speaker: F
text: Uh ,
speaker: F
text: so , um ,
speaker: F
text: you know , you should <disfmarker> you should probably not run more than , say ten jobs yourself at any one time ,
speaker: F
text: uh , just because then it would keep other people <disfmarker>
speaker: G
text: Oh ,
speaker: G
text: too much file transfer and stuff .
speaker: F
text: Well it 's not that so much as that , you know , e with <disfmarker> if everybody ran fifty jobs at once then it would just bring everything to a halt
speaker: F
text: and , you know , people 's jobs would get delayed ,
speaker: F
text: so it 's sort of a sharing thing .
speaker: G
text: OK .
speaker: F
text: Um ,
speaker: F
text: so you should try to limit it to somet sometim some number around ten jobs at a time .
speaker: F
text: Um .
speaker: F
text: So if you had a script for example that had a thousand things it needed to run , um , you 'd somehow need to put some logic in there if you were gonna use " run command " , uh , to only have ten of those going at a time .
speaker: F
text: And uh , then , when one of those finished you 'd fire off another one .
speaker: F
text: Um ,
speaker: C
text: I remember I <disfmarker> I forget whether it was when the Rutgers or <disfmarker> or Hopkins workshop ,
speaker: C
text: I remember one of the workshops I was at there were <disfmarker> everybody was real excited cuz they got twenty - five machines
speaker: C
text: and there was some kind of P - make like thing that sit sent things out .
speaker: F
text: Mm - hmm .
speaker: F
text: Mm - hmm .
speaker: F
text: Mm - hmm .
speaker: C
text: So all twenty - five people were sending things to all twenty - five machines
speaker: F
text: Yeah .
speaker: F
text: Yeah .
speaker: C
text: and <vocalsound> and things were a lot less efficient than if you 'd just use your own machine .
speaker: F
text: Yep .
speaker: F
text: Yeah , exactly .
speaker: F
text: Yeah , you have to be a little bit careful .
speaker: C
text: as I recall ,
speaker: C
text: but .
speaker: D
text: Hmm .
speaker: C
text: Yeah .
speaker: F
text: Um ,
speaker: F
text: but uh , you can also <disfmarker>
speaker: F
text: If you have that level of parallelization um , and you don't wanna have to worry about writing the logic in <disfmarker> in a Perl script to take care of that , you can use um , P - make
speaker: G
text: Just do P - make .
speaker: F
text: and <disfmarker> and you basically write a Make file that uh , you know your final job depends on these one thousand things ,
speaker: G
text: s
speaker: G
text: Mm - hmm .
speaker: F
text: and when you run P - make , uh , on your Make file , you can give it the dash capital J and <disfmarker> and then a number ,
speaker: G
text: Mm - hmm .
speaker: F
text: and that number represents how many uh , machines to use at once .
speaker: G
text: Right .
speaker: F
text: And then it 'll make sure that it never goes above that .
speaker: G
text: Right .
speaker: G
text: OK .
speaker: F
text: So ,
speaker: F
text: I can get some documentation .
speaker: D
text: So it <disfmarker> it 's <disfmarker> it 's not systematically queued .
speaker: D
text: I mean all the jobs are running .
speaker: D
text: If you launch twenty jobs , they are all running .
speaker: F
text: It depends .
speaker: D
text: Alright .
speaker: F
text: If you <disfmarker> " Run command " , that I mentioned before , is <disfmarker> doesn't know about other things that you might be running .
speaker: D
text: Uh - huh .
speaker: F
text: So , it would be possible to run a hundred run jobs at once ,
speaker: D
text: Right .
speaker: F
text: and they wouldn't know about each other .
speaker: F
text: But if you use P - make , then , it knows about all the jobs that it has to run
speaker: D
text: Mm - hmm .
speaker: F
text: and it can control , uh , how many it runs simultaneously .
speaker: C
text: So " run command " doesn't use P - make , or <disfmarker> ?
speaker: F
text: It uses " export " underlyingly .
speaker: F
text: But , if you <disfmarker>
speaker: F
text: i It 's meant to be run one job at a time ?
speaker: F
text: So you could fire off a thousand of those ,
speaker: F
text: and it doesn't know <disfmarker> any one of those doesn't know about the other ones that are running .
speaker: C
text: So why would one use that rather than P - make ?
speaker: F
text: Well , if you have ,
speaker: F
text: um <disfmarker>
speaker: F
text: Like , for example , uh if you didn't wanna write a P - make script
speaker: F
text: and you just had a , uh <disfmarker> an HTK training job that you know is gonna take uh , six hours to run ,
speaker: F
text: and somebody 's using , uh , the machine you typically use ,
speaker: F
text: you can say " run command " and your HTK thing
speaker: F
text: and it 'll find another machine ,
speaker: F
text: the fastest currently available machine
speaker: F
text: and <disfmarker> and run your job there .
speaker: C
text: Now , does it have the same sort of behavior as P - make ,
speaker: C
text: which is that , you know , if you run something on somebody 's machine and they come in and hit a key then it <disfmarker>
speaker: F
text: Yes .
speaker: F
text: Yeah ,
speaker: F
text: there are um <disfmarker>
speaker: F
text: Right .
speaker: F
text: So some of the machines at the institute , um , have this attribute called " no evict " .
speaker: F
text: And if you specify that , in <disfmarker> in one of your attribute lines , then it 'll go to a machine which your job won't be evicted from .
speaker: C
text: Mm - hmm .
speaker: F
text: But , the machines that don't have that attribute , if a job gets fired up on that , which could be somebody 's desktop machine , and <disfmarker> and they were at lunch ,
speaker: C
text: Mm - hmm .
speaker: F
text: they come back from lunch and they start typing on the console , then your machine will get evicted <disfmarker> your job <comment> will get evicted from their machine and be restarted on another machine . Automatically .
speaker: F
text: So <disfmarker> which can cause you to lose time ,
speaker: F
text: right ?
speaker: F
text: If you had a two hour job , and it got halfway through
speaker: F
text: and then somebody came back to their machine and it got evicted .
speaker: F
text: So . If you don't want your job to run on a machine where it could be evicted , then you give it the minus <disfmarker> the attribute , you know , " no evict " ,
speaker: F
text: and it 'll pick a machine that it can't be evicted from .
speaker: F
text: So .
speaker: C
text: Um , what <disfmarker> what about <disfmarker>
speaker: C
text: I remember always used to be an issue , maybe it 's not anymore , that if you <disfmarker> if something required <disfmarker> if your machine required somebody hitting a key in order to evict things that are on it so you could work ,
speaker: F
text: Mm - hmm .
speaker: C
text: but if you were logged into it from home ?
speaker: C
text: and you weren't hitting any keys ? cuz you were , home ?
speaker: F
text: Yeah ,
speaker: F
text: I <disfmarker> I 'm not sure how that works .
speaker: F
text: Uh , it seems like Andreas did something for that .
speaker: C
text: Yeah .
speaker: C
text: Hmm .
speaker: F
text: Um .
speaker: C
text: OK .
speaker: C
text: We can ask him sometime .
speaker: F
text: But <disfmarker>
speaker: F
text: Yeah .
speaker: F
text: I don't know whether it monitors the keyboard or actually looks at the console TTY ,
speaker: F
text: so maybe if you echoed something to the you know , dev <disfmarker> dev console or something .
speaker: C
text: You probably wouldn't ordinarily , though .
speaker: C
text: Yeah . Right ?
speaker: F
text: Hmm ?
speaker: C
text: You probably wouldn't ordinarily .
speaker: C
text: I mean you sort of <disfmarker>
speaker: C
text: you 're at home and you 're trying to log in ,
speaker: C
text: and it takes forever to even log you in ,
speaker: F
text: Yeah , yeah .
speaker: C
text: and you probably go , " screw this " ,
speaker: C
text: and <disfmarker> <vocalsound> You know .
speaker: F
text: Yeah .
speaker: F
text: Yeah ,
speaker: F
text: so , um ,
speaker: C
text: Yeah .
speaker: F
text: yeah .
speaker: F
text: I <disfmarker> I can <disfmarker> I 'm not sure about that one .
speaker: C
text: yeah .
speaker: F
text: But uh .
speaker: C
text: OK .
speaker: A
text: Uh , I need a little orientation about this environment and uh scr s how to run some jobs here
speaker: A
text: because I never d did anything so far with this X emissions
speaker: F
text: OK .
speaker: A
text: So ,
speaker: A
text: I think maybe I 'll ask you after the meeting .
speaker: F
text: Um .
speaker: F
text: Yeah .
speaker: F
text: Yeah , and <disfmarker> and also uh , Stephane 's a <disfmarker> a really good resource for that if you can't find me .
speaker: A
text: Yeah , yeah , yeah .
speaker: A
text: Yep .
speaker: A
text: OK ,
speaker: A
text: sure
speaker: D
text: Mmm .
speaker: F
text: Especially with regard to the Aurora stuff .
speaker: A
text: OK .
speaker: F
text: He <disfmarker> he knows that stuff better than I do .
speaker: C
text: OK .
speaker: C
text: Well , why don't we uh ,
speaker: C
text: uh ,
speaker: C
text: Sunil since you 're <vocalsound> haven't <disfmarker> haven't been at one of these yet , why don't yo you tell us what 's <disfmarker> what 's up with you ?
speaker: C
text: Wh - what you 've been up to , hopefully .
speaker: A
text: Um .
speaker: A
text: Yeah .
speaker: A
text: So ,
speaker: A
text: uh , shall I start from <disfmarker>
speaker: A
text: Well I don't know how may I <disfmarker> how <disfmarker>
speaker: A
text: OK .
speaker: A
text: Uh , I think I 'll start from the post uh Aurora submission maybe .
speaker: C
text: Yeah .
speaker: A
text: Uh , yeah ,
speaker: A
text: after the submission the <disfmarker> what I 've been working on mainly was to take <disfmarker> take other s submissions
speaker: A
text: and then
speaker: A
text: over their system , what they submitted ,
speaker: A
text: because we didn't have any speech enhancement system in <disfmarker> in ours .
speaker: A
text: So <disfmarker>
speaker: A
text: So I tried
speaker: A
text: uh ,
speaker: A
text: And u First I tried just LDA .
speaker: A
text: And then I found that
speaker: A
text: uh ,
speaker: A
text: I mean , if <disfmarker> if I combine it with LDA , it gives @ @ improvement over theirs .
speaker: A
text: Uh <disfmarker>
speaker: F
text: Are y are you saying LDA ?
speaker: A
text: Yeah .
speaker: F
text: LDA .
speaker: F
text: OK .
speaker: A
text: Yeah .
speaker: A
text: So ,
speaker: A
text: just <disfmarker> just the LDA filters .
speaker: A
text: I just plug in <disfmarker> I just take the cepstral coefficients coming from their system and then plug in LDA on top of that .
speaker: F
text: Mm - hmm .
speaker: A
text: But the LDA filter that I used was different from what we submitted in the proposal .
speaker: A
text: What I did was <vocalsound> I took the LDA filter 's design using clean speech ,
speaker: A
text: uh , mainly because the speech is already cleaned up after the enhancement
speaker: A
text: so , instead of using this , uh , narrow <disfmarker> narrow band LDA filter that we submitted uh , I got new filters .
speaker: A
text: So
speaker: A
text: that seems to be giving <disfmarker> uh , improving over their uh , system .
speaker: A
text: Slightly .
speaker: A
text: But , not very significantly .
speaker: A
text: And uh , that was
speaker: A
text: uh , showing any improvement over <disfmarker> final <disfmarker> by plugging in an LDA .
speaker: A
text: And uh ,
speaker: A
text: so then after <disfmarker> after that I <disfmarker> I added uh , on - line normalization also on top of that .
speaker: A
text: And that <disfmarker> there <disfmarker> there also I n I found that I have to make some changes to their time constant that I used
speaker: A
text: because th it has a <disfmarker> a mean and variance update time constant
speaker: A
text: and <disfmarker> which is not suitable for the enhanced speech , and whatever we try it on with proposal - one .
speaker: A
text: But um ,
speaker: A
text: I didn't <disfmarker> I didn't play with that time constant a lot ,
speaker: A
text: I just t g
speaker: A
text: I just found that I have to reduce the value <disfmarker>
speaker: A
text: I mean , I have to increase the time constant , or reduce the value of the update value .
speaker: A
text: That 's all I found
speaker: A
text: So I have to .
speaker: A
text: Uh ,
speaker: A
text: Yeah .
speaker: A
text: And uh ,
speaker: A
text: uh , the other <disfmarker> other thing what I tried was , I just um , uh , took the baseline and then ran it with the endpoint inf uh th information ,
speaker: A
text: just the Aurora baseline ,
speaker: A
text: to see that how much the baseline itself improves
speaker: A
text: by just supplying the information of the <disfmarker> I mean the w speech and nonspeech .
speaker: A
text: And uh ,
speaker: A
text: I found that the baseline itself improves by twenty - two percent by just giving the wuh .
speaker: C
text: Uh , can you back up a second ,
speaker: C
text: I <disfmarker> I <disfmarker> I missed something ,
speaker: C
text: uh ,
speaker: C
text: I guess my mind wandered .
speaker: C
text: Ad - ad When you added the on - line normalization and so forth , uh , uh things got better again ?
speaker: A
text: Yeah .
speaker: C
text: or is it ?
speaker: A
text: No .
speaker: A
text: No .
speaker: C
text: Did it not ?
speaker: A
text: No , things didn't get better with the same time constant that we used .
speaker: C
text: No , no .
speaker: C
text: With a different time constant .
speaker: A
text: With the different time constant I found that <disfmarker>
speaker: A
text: I mean , I didn't get an improvement over not using on - line normalization ,
speaker: C
text: Oh .
speaker: C
text: No you didn't ,
speaker: A
text: because I <disfmarker> I found that I would have change the value of the update factor .
speaker: C
text: OK .
speaker: A
text: But I didn't play it with play <disfmarker> play quite a bit to make it better than .
speaker: C
text: Yeah .
speaker: C
text: OK .
speaker: A
text: So , it 's still not <disfmarker>
speaker: A
text: I mean , the on - line normalization didn't give me any improvement .
speaker: C
text: OK .
speaker: A
text: And uh ,
speaker: C
text: OK .
speaker: A
text: so ,
speaker: A
text: oh yeah
speaker: A
text: So I just stopped there with the uh , speech enhancement .
speaker: A
text: The <disfmarker> the other thing what I tried was the <disfmarker> adding the uh , endpoint information to the baseline
speaker: A
text: and that itself gives like twenty - two percent
speaker: A
text: because the <disfmarker> the second <disfmarker> the new phase is going to be with the endpointed speech .
speaker: A
text: And just to get a feel of how much the baseline itself is going to change by adding this endpoint information , I just , uh , use <disfmarker>
speaker: C
text: Hmm .
speaker: F
text: So people won't even have to worry about , uh , doing speech - nonspeech then .
speaker: A
text: Yeah
speaker: A
text: that 's , that 's what the feeling is like .
speaker: F
text: Mmm .
speaker: A
text: They 're going to give the endpoint information .
speaker: F
text: I see .
speaker: C
text: G I guess the issue is that people do that anyway ,
speaker: C
text: everybody does that ,
speaker: C
text: and they wanted to see , given that you 're doing that , what <disfmarker> what are the best features that you should use .
speaker: A
text: Yeah .
speaker: F
text: Yeah ,
speaker: F
text: I see .
speaker: A
text: So ,
speaker: C
text: I mean clearly they 're interact .
speaker: C
text: So I don't know that I entirely agree with it .
speaker: F
text: Yeah .
speaker: C
text: But <disfmarker> but it might be uh <disfmarker> In some ways it might be better t to <disfmarker> rather than giving the endpoints , to have a standard that everybody uses and then interacts with .
speaker: F
text: Mm - hmm .
speaker: C
text: But , you know . It 's <disfmarker> it 's still someth reasonable .
speaker: F
text: So , are people supposed to assume that there is uh <disfmarker>
speaker: F
text: Are <disfmarker> are people not supposed to use any speech outside of those endpoints ?
speaker: A
text: Uh <disfmarker>
speaker: F
text: Or can you then use speech outside of it for estimating background noise and things ?
speaker: A
text: No .
speaker: A
text: No .
speaker: A
text: That i I <disfmarker>
speaker: A
text: Yeah .
speaker: A
text: Yeah , yeah ,
speaker: A
text: exactly .
speaker: A
text: I guess that is <disfmarker> that is where the consensus is .
speaker: A
text: Like y you will <disfmarker> you will <disfmarker> You 'll be given the information about the beginning and the end of speech
speaker: A
text: but the whole speech is available to you .
speaker: F
text: OK .
speaker: A
text: So .
speaker: C
text: So it should make the spectral subtraction style things work even better ,
speaker: C
text: because you don't have the mistakes in it .
speaker: A
text: Yeah .
speaker: C
text: Yeah ?
speaker: C
text: OK .
speaker: A
text: Yeah .
speaker: A
text: So <disfmarker>
speaker: A
text: So that <disfmarker> that <disfmarker> The baseline itself <disfmarker>
speaker: A
text: I mean , it improves by twenty - two percent .
speaker: A
text: I found that in s one of the SpeechDat - Car cases , that like , the Spanish one improves by just fifty percent by just putting the endpoint .
speaker: F
text: Wow .
speaker: A
text: w
speaker: A
text: I mean you don't need any further speech enhancement with fifty .
speaker: A
text: So , uh ,
speaker: F
text: So the baseline itself improves by fifty percent .
speaker: A
text: Yeah , by fifty percent .
speaker: C
text: Yeah .
speaker: C
text: So it 's g it 's gonna be harder to <vocalsound> beat that actually .
speaker: F
text: Wow .
speaker: F
text: Yeah .
speaker: A
text: Yeah ,
speaker: C
text: But <disfmarker> but <disfmarker>
speaker: A
text: so <disfmarker>
speaker: A
text: so that is when uh , the <disfmarker> the qualification criteria was reduced from fifty percent to something like twenty - five percent for well - matched .
speaker: A
text: And I think they have <disfmarker> they have actually changed their qualification c criteria now .
speaker: A
text: And uh ,
speaker: A
text: Yeah ,
speaker: A
text: I guess after that , I just went home f I just had a vacation fo for four weeks .
speaker: C
text: OK .
speaker: A
text: Uh .
speaker: C
text: No , that 's <disfmarker> that 's <disfmarker> that 's a good <disfmarker> good update .
speaker: A
text: Ye
speaker: A
text: Yeah ,
speaker: A
text: and I <disfmarker> I came back and I started working on uh , some other speech enhancement algorithm .
speaker: A
text: I mean , so <disfmarker> I <disfmarker> from the submission what I found that people have tried spectral subtraction and Wiener filtering . These are the main uh , approaches where people have tried ,
speaker: C
text: Yeah .
speaker: A
text: so just to <disfmarker> just to fill the space with some f few more speech enhancement algorithms to see whether it improves a lot , I <disfmarker> I 've been working on this uh , signal subspace approach for speech enhancement
speaker: A
text: where you take the noisy signal and then decomposing the signal s and the noise subspace
speaker: A
text: and then try to estimate the clean speech from the signal plus noise subspace .
speaker: C
text: Mm - hmm .
speaker: A
text: And <disfmarker>
speaker: A
text: So , I 've been actually running some s
speaker: A
text: So far I 've been trying it only on Matlab .
speaker: A
text: I have to <disfmarker> to <disfmarker> to test whether it works first or not
speaker: C
text: Yeah .
speaker: A
text: and then I 'll p port it to C
speaker: A
text: and I 'll update it with the repository once I find it it giving any some positive result .
speaker: A
text: So , yeah .
speaker: C
text: S
speaker: C
text: So you s you So you said one thing I want to jump on for a second .
speaker: C
text: So <disfmarker> so now you 're <disfmarker> you 're getting tuned into the repository thing that he has here
speaker: A
text: Yeah .
speaker: C
text: and <disfmarker> so we we 'll have a <vocalsound> single place where the stuff is .
speaker: A
text: Yep .
speaker: A
text: Yeah .
speaker: C
text: Cool .
speaker: C
text: Um ,
speaker: C
text: so maybe uh , just briefly , you could remind us about the related experiments .
speaker: C
text: Cuz you did some stuff that you talked about last week ,
speaker: C
text: I guess ?
speaker: D
text: Mm - hmm .
speaker: C
text: Um , where you were also combining something <disfmarker>
speaker: C
text: both of you I guess were both combining something from the uh , French Telecom system with <vocalsound> the u uh <disfmarker>
speaker: D
text: Right .
speaker: C
text: I <disfmarker> I don't know whether it was system one or system two ,
speaker: C
text: or <disfmarker> ?
speaker: D
text: Mm - hmm .
speaker: D
text: It was system one .
speaker: D
text: So
speaker: C
text: OK .
speaker: D
text: we <disfmarker>
speaker: D
text: The main thing that we did is just to take the spectral subtraction from the France Telecom ,
speaker: D
text: which provide us some speech samples that are uh , with noise removed .
speaker: C
text: So I let me <disfmarker> let me just stop you there .
speaker: C
text: So then , one distinction is that uh , you were taking the actual France Telecom features and then applying something to <disfmarker>
speaker: A
text: Uh , no
speaker: A
text: there is a slight different .
speaker: A
text: Uh
speaker: A
text: I mean , which are extracted at the handset
speaker: C
text: Yeah .
speaker: A
text: because they had another back - end blind equalization <disfmarker>
speaker: C
text: Yeah .
speaker: C
text: But that 's what I mean .
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: C
text: But u u
speaker: C
text: Sorry ,
speaker: C
text: yeah , I 'm not being <disfmarker> I 'm not being clear .
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: C
text: What I meant was you had something like cepstra or something , right ?
speaker: A
text: Yeah , yeah , yeah , yeah .
speaker: C
text: And so one difference is that , I guess you were taking spectra .
speaker: A
text: The speech .
speaker: B
text: Yeah .
speaker: D
text: Yeah .
speaker: D
text: But I guess it 's the s exactly the same thing
speaker: D
text: because on the heads uh , handset they just applied this Wiener filter and then compute cepstral features ,
speaker: D
text: right ?
speaker: A
text: Yeah ,
speaker: D
text: or <disfmarker> ?
speaker: A
text: the cepstral f The difference is like <disfmarker> There may be a slight difference in the way <disfmarker>
speaker: A
text: because they use exactly the baseline system for converting the cepstrum once you have the speech .
speaker: D
text: Right .
speaker: A
text: I mean , if we are using our own code for th I mean that <disfmarker> that could be the only difference .
speaker: A
text: I mean , there is no other difference .
speaker: D
text: Mm - hmm .
speaker: A
text: Yeah .
speaker: C
text: But you got some sort of different result .
speaker: C
text: So I 'm trying to understand it .
speaker: C
text: But uh ,
speaker: D
text: Yeah , well I think we should uh , have a table with all the result
speaker: C
text: I th
speaker: D
text: because I don't know I uh , I don't exactly know what are your results ?
speaker: D
text: But ,
speaker: A
text: OK .
speaker: A
text: OK .
speaker: D
text: Mmm .
speaker: D
text: Yeah , but so we did this ,
speaker: D
text: and another difference I guess is that we just applied uh , proposal - one system after this
speaker: D
text: without <disfmarker> well , with our modification to reduce the delay of the <disfmarker> the LDA filters ,
speaker: D
text: and
speaker: A
text: Uh - huh .
speaker: D
text: Well there are slight modifications ,
speaker: B
text: And the filter <disfmarker>
speaker: D
text: but it was the full proposal - one .
speaker: D
text: In your case , if you tried just putting LDA , then maybe on - line normalization <disfmarker> ?
speaker: A
text: Only LDA .
speaker: A
text: Yeah .
speaker: A
text: Af - I <disfmarker> after that I added on - line normalization ,
speaker: A
text: yeah .
speaker: D
text: Mm - hmm .
speaker: D
text: So we just tried directly to <disfmarker> to just , keep the system as it was
speaker: D
text: and ,
speaker: D
text: um ,
speaker: D
text: when we plug the spectral subtraction it improves uh , signif significantly .
speaker: D
text: Um ,
speaker: D
text: but , what seems clear also is that we have to retune the time constants of the on - line normalization .
speaker: A
text: Yeah , yeah .
speaker: A
text: Yeah .
speaker: D
text: Because if we keep the value that was submitted uh , it doesn't help at all .
speaker: D
text: You can remove on - line normalization , or put it , it doesn't change anything .
speaker: D
text: Uh , uh , as long as you have the spectral subtraction .
speaker: D
text: But , you can still find some kind of optimum somewhere ,
speaker: D
text: and we don't know where exactly
speaker: D
text: but ,
speaker: A
text: Yeah .
speaker: D
text: uh .
speaker: A
text: Yeah , I assume .
speaker: C
text: So it sounds like you should look at some tables of results or something
speaker: D
text: Right .
speaker: C
text: and see where i where the <disfmarker> <vocalsound> where they were different and what we can learn from it .
speaker: D
text: Yeah .
speaker: A
text: Yeah .
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: A
text: without any change .
speaker: D
text: Yeah .
speaker: B
text: But it 's <disfmarker>
speaker: A
text: OK .
speaker: D
text: Well ,
speaker: B
text: It 's the new .
speaker: D
text: with <disfmarker> with <disfmarker> with changes ,
speaker: A
text: with
speaker: B
text: The new .
speaker: D
text: because we change it the system to have <disfmarker>
speaker: A
text: Oh yeah ,
speaker: A
text: I mean the <disfmarker> the new LDA filters .
speaker: B
text: The new .
speaker: A
text: I mean <disfmarker>
speaker: D
text: Yeah .
speaker: A
text: OK .
speaker: D
text: LDA filters .
speaker: D
text: There are other things that we finally were shown to improve also
speaker: D
text: like , the sixty - four hertz cut - off .
speaker: B
text: Mm - hmm .
speaker: A
text: Mm - hmm .
speaker: D
text: w Uh , it doesn't seem to hurt on TI - digits , finally .
speaker: A
text: OK .
speaker: D
text: Maybe because of other changes .
speaker: A
text: OK .
speaker: D
text: Um ,
speaker: D
text: well there are some <vocalsound> minor changes ,
speaker: D
text: yeah .
speaker: A
text: Mm - hmm .
speaker: D
text: And , right now if we look at the results , it 's , um , always better than <disfmarker> it seems always better than France Telecom for mismatch and high - mismatch .
speaker: D
text: And it 's still slightly worse for well - matched .
speaker: D
text: Um ,
speaker: B
text: But
speaker: D
text: but this is not significant .
speaker: D
text: But , the problem is that it 's not significant ,
speaker: D
text: but if you put this in the , mmm , uh , spreadsheet , it 's still worse .
speaker: D
text: Even with very minor <disfmarker>
speaker: D
text: uh ,
speaker: D
text: even if it 's only slightly worse for well - matched .
speaker: C
text: Mm - hmm .
speaker: D
text: And significantly better for HM .
speaker: D
text: Uh ,
speaker: D
text: but , well .
speaker: D
text: I don't think it 's importa important
speaker: D
text: because when they will change their metric ,
speaker: D
text: uh ,
speaker: D
text: uh , mainly because of uh , when you p you plug the um , frame dropping in the baseline system , it will improve a lot HM , and MM ,
speaker: A
text: Yeah .
speaker: D
text: so ,
speaker: D
text: um , I guess what will happen <disfmarker>
speaker: D
text: I don't know what will happen .
speaker: D
text: But , the different contribution , I think , for the different test set will be more even .
speaker: A
text: Because the <disfmarker> your improvement on HM and MM will also go down significantly in the spreadsheet
speaker: A
text: so .
speaker: A
text: But the <pause> the well - matched may still <disfmarker>
speaker: D
text: Mm - hmm .
speaker: A
text: I mean the well - matched may be the one which is least affected by adding the endpoint information .
speaker: C
text: Right .
speaker: A
text: Yeah .
speaker: A
text: So the <disfmarker>
speaker: A
text: the MM <disfmarker>
speaker: D
text: Mm - hmm .
speaker: A
text: MM and HM are going to be v hugely affected by it .
speaker: D
text: Yeah ,
speaker: D
text: so um ,
speaker: A
text: Yeah .
speaker: D
text: yeah .
speaker: A
text: Yeah .
speaker: A
text: But they d the <disfmarker> everything I mean is like ,
speaker: A
text: but there
speaker: A
text: that 's how they reduce <disfmarker> why they reduce the qualification to twenty - five percent or some <disfmarker> something on .
speaker: D
text: Mm - hmm .
speaker: C
text: But are they changing the weighting ?
speaker: A
text: Uh , no ,
speaker: A
text: I guess they are going ahead with the same weighting .
speaker: D
text: Yeah .
speaker: A
text: Yeah .
speaker: A
text: So there 's nothing on <disfmarker>
speaker: C
text: I don't understand that .
speaker: C
text: I guess I <disfmarker> I haven't been part of the discussion ,
speaker: A
text: Yeah .
speaker: C
text: so , um ,
speaker: C
text: it seems to me that the well - matched condition is gonna be unusual ,
speaker: C
text: in this case .
speaker: A
text: Usual .
speaker: C
text: Unusual .
speaker: A
text: Uh - huh .
speaker: C
text: Because , um , you don't actually have good matches ordinarily for what any @ @ <disfmarker> particular person 's car is like ,
speaker: C
text: or
speaker: A
text: Mmm .
speaker: C
text: uh ,
speaker: A
text: Mmm .
speaker: C
text: It seems like something like the middle one is <disfmarker> is more natural .
speaker: A
text: Hmm .
speaker: C
text: So I don't know why the <pause> well - matched is
speaker: A
text: Right .
speaker: C
text: uh <disfmarker>
speaker: D
text: Mm - hmm .
speaker: A
text: Yeah , but actually the well <disfmarker> well the well - matched
speaker: A
text: um ,
speaker: A
text: uh ,
speaker: A
text: I mean the <disfmarker> the well - matched condition is not like , uh , the one in TI - digits
speaker: A
text: where uh , you have all the training , uh , conditions exactly like replicated in the testing condition also .
speaker: A
text: It 's like , this is not calibrated by SNR or something .
speaker: A
text: The well - matched has also some <disfmarker> some mismatch in that which is other than the <disfmarker>
speaker: C
text: The well wa matched has mismatch ?
speaker: A
text: has <disfmarker> has also some slight mismatches ,
speaker: A
text: unlike the TI - digits where it 's like prefectly matched
speaker: F
text: Perfect to match .
speaker: C
text: Yeah .
speaker: A
text: because it 's artificially added noise .
speaker: C
text: Yeah .
speaker: A
text: But this is natural recording .
speaker: C
text: So remind me of what well - matched meant ?
speaker: C
text: You 've told me many times .
speaker: A
text: The <disfmarker> the well - matched is like <disfmarker>
speaker: A
text: the <disfmarker> the well - matched is defined like it 's seventy percent of the whole database is used for training and thirty percent for testing .
speaker: D
text: Yeah .
speaker: D
text: Well , so it means that if the database is large enough , it 's matched .
speaker: A
text: It 's <disfmarker> it 's <disfmarker>
speaker: D
text: Because it
speaker: A
text: OK ,
speaker: A
text: it 's <disfmarker>
speaker: C
text: Yeah .
speaker: D
text: in each set you have a range of conditions <disfmarker>
speaker: D
text: Well <disfmarker>
speaker: C
text: Right .
speaker: C
text: So , I mean ,
speaker: C
text: yeah , unless they deliberately chose it to be different , which they didn't because they want it to be well - matched ,
speaker: C
text: it is pretty much <disfmarker>
speaker: C
text: You know , so it 's <disfmarker> so it 's sort of saying if you <disfmarker>
speaker: F
text: It 's <disfmarker> it 's not guaranteed though .
speaker: A
text: Yeah .
speaker: C
text: Uh , it 's not guaranteed .
speaker: A
text: Yeah .
speaker: C
text: Right .
speaker: D
text: Mm - hmm .
speaker: C
text: Right .
speaker: A
text: Yeah because the m the main <disfmarker> major reason for the m
speaker: A
text: the main mismatch is coming from the amount of noise and the silence frames and all those present in the database actually .
speaker: C
text: Again , if you have enough <disfmarker> if you have enough <disfmarker>
speaker: A
text: No
speaker: A
text: yeah , yeah .
speaker: A
text: Yeah .
speaker: C
text: So it 's sort of i i it 's sort of saying OK ,
speaker: C
text: so you <disfmarker> much as you train your dictation machine for talking into your computer , um , you <disfmarker> you have a car ,
speaker: C
text: and so you drive it around a bunch and <disfmarker> and record noise conditions , or something ,
speaker: C
text: and then <disfmarker> I don't think that 's very realistic ,
speaker: C
text: I mean I th
speaker: A
text: Mm - hmm .
speaker: C
text: I <disfmarker> I you know ,
speaker: C
text: so I <disfmarker>
speaker: C
text: I <disfmarker> I <disfmarker> you know ,
speaker: C
text: I guess they 're saying that if you were a company that was selling the stuff commercially , that you would have a bunch of people driving around in a bunch of cars ,
speaker: C
text: and <disfmarker> and you would have something that was roughly similar
speaker: C
text: and maybe that 's the argument ,
speaker: C
text: but I 'm not sure I buy it ,
speaker: C
text: so .
speaker: A
text: Yeah , yeah , yeah .
speaker: C
text: Uh ,
speaker: C
text: So
speaker: C
text: What else is going on ?
speaker: D
text: Mmm .
speaker: D
text: You
speaker: D
text: Yeah .
speaker: D
text: We are playing <disfmarker> we are also playing , trying to put other spectral subtraction mmm , in the code .
speaker: D
text: Um ,
speaker: D
text: it would be a very simple spectral subtraction , on the um , mel energies
speaker: D
text: which I already tested but without the um frame dropping actually ,
speaker: D
text: and I think it 's important to have frame dropping if you use spectral subtraction .
speaker: F
text: Is it <disfmarker> is spectral subtraction typically done on the <disfmarker> after the mel , uh , scaling
speaker: F
text: or is it done on the FFT bins ?
speaker: D
text: Um ,
speaker: F
text: Does it matter ,
speaker: F
text: or <disfmarker> ?
speaker: D
text: I d
speaker: D
text: I don't know .
speaker: D
text: Well , it 's both <disfmarker> both uh , cases can i
speaker: F
text: Oh .
speaker: D
text: Yeah .
speaker: D
text: So - some of the proposal , uh , we 're doing this on the bin <disfmarker> on the FFT bins ,
speaker: F
text: Hmm .
speaker: D
text: others on the um , mel energies .
speaker: D
text: You can do both ,
speaker: D
text: but I cannot tell you what 's <disfmarker> which one might be better
speaker: D
text: or <disfmarker>
speaker: F
text: Hmm .
speaker: D
text: I <disfmarker>
speaker: A
text: I guess if you want to reconstruct the speech , it may be a good idea to do it on FFT bins .
speaker: D
text: I don't know .
speaker: F
text: Mmm .
speaker: D
text: Yeah ,
speaker: D
text: but
speaker: A
text: But for speech recognition , it may not .
speaker: A
text: I mean it may not be very different if you do it on mel warped or whether you do it on FFT .
speaker: F
text: I see .
speaker: A
text: So you 're going to do a linear weighting anyway after that .
speaker: A
text: Well <disfmarker>
speaker: A
text: Yeah ?
speaker: F
text: Hmm .
speaker: A
text: So , it may not be really a big different .
speaker: D
text: Well , it gives something different ,
speaker: D
text: but I don't know what are the , pros and cons of both .
speaker: A
text: It
speaker: A
text: I Uh - huh .
speaker: C
text: Hmm .
speaker: A
text: So
speaker: C
text: OK .
speaker: A
text: The other thing is like when you 're putting in a speech enhancement technique ,
speaker: A
text: uh ,
speaker: A
text: is it like one stage speech enhancement ?
speaker: A
text: Because everybody seems to have a mod two stages of speech enhancement in all the proposals ,
speaker: A
text: which is really giving them some improvement .
speaker: D
text: Yeah .
speaker: B
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: A
text: I mean they just do the same thing again once more .
speaker: C
text: Mm - hmm .
speaker: A
text: And <disfmarker> So , there 's something that is good about doing it <disfmarker>
speaker: A
text: I mean , to cleaning it up once more .
speaker: D
text: Yeah , it might be .
speaker: D
text: Yeah .
speaker: A
text: Yeah ,
speaker: A
text: so we can <disfmarker>
speaker: D
text: So maybe in my implementation I should also try to inspire me from this kind of thing
speaker: A
text: Yeah .
speaker: A
text: That 's what
speaker: D
text: and <disfmarker> Yeah .
speaker: C
text: Well , the other thing would be to combine what you 're doing .
speaker: C
text: I mean maybe one or <disfmarker> one or the other of the things that you 're doing would benefit from the other happening first .
speaker: A
text: That 's wh
speaker: A
text: Yeah .
speaker: A
text: So ,
speaker: C
text: Right ,
speaker: C
text: so he 's doing a signal subspace thing ,
speaker: C
text: maybe it would work better if you 'd already done some simple spectral subtraction ,
speaker: C
text: or maybe vi maybe the other way around ,
speaker: D
text: Yeah ,
speaker: D
text: mm - hmm .
speaker: A
text: Yeah .
speaker: C
text: you know ?
speaker: D
text: Mm - hmm .
speaker: A
text: So I 've been thinking about combining the Wiener filtering with signal subspace ,
speaker: A
text: I mean just to see all <disfmarker> some <disfmarker> some such permutation combination to see whether it really helps or not .
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: D
text: Mm - hmm .
speaker: D
text: Yeah .
speaker: D
text: Yeah .
speaker: C
text: How is it <disfmarker>
speaker: C
text: I <disfmarker> I guess I 'm ignorant about this ,
speaker: C
text: how does <disfmarker>
speaker: C
text: I mean , since Wiener filter also assumes that you 're <disfmarker> that you 're adding together the two signals , how is <disfmarker> how is that differ from signal subspace ?
speaker: A
text: The signal subspace ?
speaker: C
text: Yeah .
speaker: A
text: The <disfmarker>
speaker: A
text: The signal subspace approach has actually an in - built Wiener filtering in it .
speaker: C
text: Oh , OK .
speaker: A
text: Yeah .
speaker: A
text: It is like a KL transform followed by a Wiener filter .
speaker: A
text: Is the signal is <disfmarker> is a signal substrate .
speaker: C
text: Oh , oh , OK
speaker: C
text: so the difference is the KL .
speaker: A
text: So , the <disfmarker> the different <disfmarker>
speaker: A
text: the c the <disfmarker> the advantage of combining two things is mainly coming from the signal subspace approach doesn't work very well if the SNR is very bad .
speaker: A
text: It 's <disfmarker>
speaker: C
text: I see .
speaker: A
text: it works very poorly with the poor SNR conditions , and in colored noise .
speaker: C
text: So essentially you could do simple spectral subtraction , followed by a KL transform , followed by a
speaker: A
text: Wiener filtering .
speaker: C
text: Wiener filter .
speaker: A
text: It 's a <disfmarker> it 's a cascade of two s
speaker: C
text: Yeah , in general , you don't <disfmarker>
speaker: C
text: that 's right
speaker: C
text: you don't wanna othorg orthogonalize if the things are noisy .
speaker: C
text: Actually .
speaker: C
text: Um , that was something that uh , Herve and I were talking about with um , the multi - band stuff ,
speaker: C
text: that if you 're converting things to from uh , bands , groups of bands into cepstral coef you know , local sort of local cepstral coefficients that it 's not that great to do it if it 's noisy .
speaker: A
text: Mm - hmm .
speaker: A
text: OK .
speaker: A
text: Yeah .
speaker: C
text: Uh ,
speaker: C
text: so .
speaker: A
text: So .
speaker: A
text: So that <disfmarker> that 's one reason maybe we could combine
speaker: A
text: s some <disfmarker> something to improve SNR a little bit , first stage ,
speaker: C
text: Yeah .
speaker: A
text: and then do a something in the second stage
speaker: A
text: which could take it further .
speaker: D
text: What was your point about <disfmarker> about colored noise there ?
speaker: A
text: Oh , the colored noise
speaker: D
text: Yeah .
speaker: A
text: uh <disfmarker>
speaker: A
text: the colored noise <disfmarker> the <disfmarker> the v the signal subspace approach has <disfmarker> I mean , it <disfmarker> it actually depends on inverting the matrices .
speaker: A
text: So it <disfmarker> it <disfmarker> ac
speaker: A
text: the covariance matrix of the noise .
speaker: D
text: Mm - hmm .
speaker: A
text: So if <disfmarker> if it is not positive definite ,
speaker: A
text: I mean it has a <disfmarker> it 's <disfmarker>
speaker: A
text: It doesn't behave very well if it is not positive definite
speaker: A
text: ak
speaker: A
text: It works very well with white noise because we know for sure that it has a positive definite .
speaker: C
text: So you should do spectral subtraction and then add noise .
speaker: A
text: So the way they get around is like they do an inverse filtering ,
speaker: A
text: first of the colo colored noise
speaker: C
text: Yeah .
speaker: A
text: and then make the noise white ,
speaker: C
text: Yeah .
speaker: A
text: and then finally when you reconstruct the speech back , you do this filtering again .
speaker: D
text: Yeah , right .
speaker: C
text: I was only half kidding .
speaker: C
text: I mean if you <disfmarker> sort of <vocalsound> you do the s spectral subtraction , that also gets rid <disfmarker>
speaker: D
text: Yeah .
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: C
text: and then you <disfmarker> then <disfmarker> then add a little bit l noise <disfmarker> noise addition <disfmarker>
speaker: C
text: I mean , that sort of what J <disfmarker> JRASTA does , in a way .
speaker: C
text: If you look at what JRASTA doing essentially i i it 's equivalent to sort of adding a little <disfmarker> adding a little noise ,
speaker: A
text: Yeah .
speaker: A
text: Huh ?
speaker: A
text: Uh - huh .
speaker: D
text: Uh - huh .
speaker: C
text: in order to get rid of the effects of noise .
speaker: A
text: So .
speaker: D
text: Yeah .
speaker: C
text: OK .
speaker: D
text: Uh , yeah .
speaker: D
text: So there is this .
speaker: D
text: And
speaker: D
text: maybe we <disfmarker>
speaker: D
text: well we find some people so that <vocalsound> uh , agree to maybe work with us ,
speaker: D
text: and they have implementation of VTS techniques
speaker: D
text: so it 's um , Vector Taylor Series that are used to mmm , <vocalsound> uh f to model the transformation between clean cepstra and noisy cepstra .
speaker: D
text: So . Well ,
speaker: D
text: if you take the standard model of channel plus noise , uh , it 's <disfmarker> it 's a nonlinear eh uh , transformation in the cepstral domain .
speaker: C
text: Mm - hmm .
speaker: C
text: Yes .
speaker: D
text: And
speaker: D
text: uh , there is a way to approximate this using uh , first - order or second - order Taylor Series
speaker: D
text: and
speaker: D
text: it can be used for <vocalsound> uh , getting rid of the noise and the channel effect .
speaker: C
text: Who is doing this ?
speaker: D
text: Uh w working in the cepstral domain ?
speaker: D
text: So there is one guy in Grenada ,
speaker: B
text: Yeah , in Grenada
speaker: D
text: and another in <pause> uh , Lucent that I met at ICASSP .
speaker: B
text: one of my friend .
speaker: D
text: uh ,
speaker: C
text: Who 's the guy in Grenada ?
speaker: B
text: Uh , Jose Carlos Segura .
speaker: C
text: I don't know him .
speaker: A
text: This VTS has been proposed by CMU ?
speaker: D
text: Mm - hmm .
speaker: A
text: Is it <disfmarker> is it the CMU ?
speaker: B
text: Yeah , yeah , yeah .
speaker: A
text: Yeah , yeah , OK .
speaker: B
text: Originally the idea was from CMU .
speaker: D
text: Mm - hmm .
speaker: A
text: From C .
speaker: D
text: Yeah .
speaker: C
text: Uh - huh .
speaker: D
text: Well , it 's again a different thing <vocalsound> <vocalsound> that could be tried .
speaker: D
text: Um ,
speaker: C
text: Uh - huh .
speaker: D
text: Mmm , yeah .
speaker: C
text: Yeah , so at any rate , you 're looking general , uh , standing back from it , looking at ways to combine one form or another of uh , noise removal , uh , with <disfmarker> with these other things we have ,
speaker: D
text: Mm - hmm .
speaker: C
text: uh , looks like a worthy thing to <disfmarker> to do here .
speaker: D
text: Uh , yeah .
speaker: D
text: But , yeah .
speaker: D
text: But for sure there 's required to <disfmarker> that requires to re - check everything else , and re - optimize the other things
speaker: C
text: Oh yeah .
speaker: D
text: and ,
speaker: D
text: for sure the on - line normalization may be the LDA filter .
speaker: D
text: Um ,
speaker: C
text: Well one of the <disfmarker> seems like one of the things to go through next week when Hari 's here ,
speaker: D
text: I <disfmarker>
speaker: C
text: cuz Hari 'll have his own ideas too <disfmarker>
speaker: D
text: Uh - huh .
speaker: C
text: or <pause> I guess not next week ,
speaker: C
text: week and a half ,
speaker: C
text: uh , will be sort of go through these alternatives ,
speaker: C
text: what we 've seen so far ,
speaker: C
text: and come up with some game plans .
speaker: C
text: Um .
speaker: C
text: You know .
speaker: C
text: So , I mean one way would <disfmarker>
speaker: C
text: he Here are some alternate visions .
speaker: C
text: I mean one would be , you look at a few things very quickly ,
speaker: C
text: you pick on something that looks like it 's promising
speaker: C
text: and then everybody works really hard on the same <disfmarker> different aspects of the same thing .
speaker: C
text: Another thing would be to have t to <disfmarker> to pick two pol two plausible things ,
speaker: C
text: and <disfmarker> and you know , have t sort of two working things for a while until we figure out what 's better ,
speaker: D
text: Mm - hmm .
speaker: C
text: and then ,
speaker: C
text: you know ,
speaker: C
text: uh ,
speaker: C
text: but , w um ,
speaker: C
text: uh , he 'll have some ideas on that too .
speaker: A
text: The other thing is to , uh <disfmarker> Most of the speech enhancement techniques have reported results on small vocabulary tasks .
speaker: A
text: But we <disfmarker> we going to address this Wall Street Journal in our next stage ,
speaker: A
text: which is also going to be a noisy task
speaker: A
text: so s very few people have reported something on using some continuous speech at all .
speaker: A
text: So , there are some <disfmarker>
speaker: A
text: I mean , I was looking at some literature on speech enhancement applied to large vocabulary tasks
speaker: A
text: and
speaker: A
text: spectral subtraction doesn't seems to be the thing to do for large vocabulary tasks .
speaker: A
text: And it 's <disfmarker>
speaker: A
text: Always people have shown improvement with Wiener filtering and maybe subspace approach over spectral subtraction everywhere .
speaker: A
text: But if we <disfmarker> if we have to use simple spectral subtraction , we may have to do some optimization <pause> to make it work @ @ .
speaker: C
text: So they 're making <disfmarker>
speaker: C
text: there <disfmarker> Somebody 's generating Wall Street Journal with additive <disfmarker> artificially added noise or something ?
speaker: A
text: Yeah , yeah .
speaker: C
text: Sort of a <disfmarker> sort of like what they did with TI - digits , and ?
speaker: A
text: Yeah .
speaker: C
text: Yeah , OK .
speaker: A
text: Yeah .
speaker: A
text: I m I guess Guenter Hirsch is in charge of that .
speaker: A
text: Guenter Hirsch and TI .
speaker: C
text: OK .
speaker: A
text: Maybe Roger <disfmarker>
speaker: A
text: r Roger , maybe in charge of .
speaker: C
text: And then they 're <disfmarker> they 're uh , uh , generating HTK scripts to <disfmarker>
speaker: A
text: Yeah .
speaker: A
text: Yeah , I don't know .
speaker: A
text: There are <disfmarker> they have <disfmarker> there is no <disfmarker>
speaker: A
text: I don't know if they are converging on HTK or are using some Mississippi State ,
speaker: C
text: Mis - Mississippi State maybe ,
speaker: A
text: yeah .
speaker: C
text: yeah .
speaker: A
text: I 'm not sure about that .
speaker: C
text: Yeah , so that 'll be a little <disfmarker> little task in itself .
speaker: A
text: Yeah .
speaker: C
text: Um ,
speaker: C
text: well we 've <disfmarker>
speaker: C
text: Yeah , it 's true for the additive noise , y artificially added noise we 've always used small vocabulary too .
speaker: C
text: But for n there 's been noisy speech this larv large vocabulary that we 've worked with in Broadcast News .
speaker: C
text: So we we did the Broadcast News evaluation
speaker: C
text: and some of the focus conditions were noisy
speaker: A
text: Mm - hmm .
speaker: C
text: and <disfmarker> and <disfmarker>
speaker: A
text: It had additive n
speaker: C
text: But we <disfmarker> but we didn't do spectral subtraction .
speaker: C
text: We were doing our funny stuff , right ?
speaker: C
text: We were doing multi multi uh , multi - stream and <disfmarker> and so forth .
speaker: A
text: Yeah .
speaker: C
text: But it , you know , we di stuff we did helped .
speaker: C
text: I mean it , did something .
speaker: C
text: So .
speaker: A
text: OK .
speaker: C
text: Um ,
speaker: C
text: now we have this um , meeting data .
speaker: C
text: You know , like the stuff we 're <comment> recording right now ,
speaker: A
text: Yeah .
speaker: A
text: Yeah .
speaker: C
text: and <disfmarker>
speaker: C
text: and uh ,
speaker: C
text: that we have uh , for the <disfmarker> uh , the quote - unquote noisy data there is just <disfmarker> noisy and reverberant actually .
speaker: C
text: It 's the far field mike .
speaker: C
text: And uh , we have uh , the digits that we do at the end of these things .
speaker: C
text: And that 's what most o
speaker: C
text: again , most of our work has been done with that ,
speaker: C
text: with <disfmarker> with uh , connected digits .
speaker: A
text: Uh - huh .
speaker: C
text: Um ,
speaker: C
text: but uh , we have recognition now with some of the continuous speech ,
speaker: C
text: large vocabulary continuous speech , using Switchboard <disfmarker> uh , Switchboard recognizer ,
speaker: A
text: Yeah .
speaker: A
text: OK .
speaker: C
text: uh ,
speaker: C
text: no training , <vocalsound> from this ,
speaker: C
text: just <disfmarker> just plain using the Switchboard .
speaker: A
text: Oh . You just take the Switchboard trained <disfmarker> ?
speaker: C
text: That 's <disfmarker> that 's what we 're doing ,
speaker: A
text: Yeah ,
speaker: C
text: yeah .
speaker: A
text: yeah .
speaker: C
text: Now there are some adaptation though ,
speaker: A
text: OK .
speaker: C
text: that <disfmarker> that uh , Andreas has been playing with ,
speaker: A
text: Yeah .
speaker: A
text: That 's cool .
speaker: A
text: OK .
speaker: C
text: but we 're hop
speaker: C
text: uh , actually uh , Dave and I were just talking earlier today about maybe at some point not that distant future , trying some of the techniques that we 've talked about on , uh , some of the large vocabulary data .
speaker: C
text: Um ,
speaker: C
text: I mean , I guess no one had done <disfmarker> yet done test one on the distant mike using uh , the SRI recognizer
speaker: C
text: and , uh ,
speaker: F
text: I don't <disfmarker>
speaker: F
text: not that I know of .
speaker: C
text: Yeah ,
speaker: C
text: cuz everybody 's scared .
speaker: A
text: Yeah .
speaker: C
text: You 'll see a little smoke coming up from the <disfmarker> the CPU or something <vocalsound> trying to <disfmarker> trying to do it ,
speaker: F
text: That 's right
speaker: C
text: but
speaker: C
text: uh , yeah .
speaker: C
text: But , you 're right
speaker: C
text: that <disfmarker> that <disfmarker> that 's a real good point , that uh , we <disfmarker> we don't know yeah , uh , I mean , what if any of these ta
speaker: C
text: I guess that 's why they 're pushing that in the uh <disfmarker> in the evaluation .
speaker: A
text: Yeah .
speaker: C
text: Uh ,
speaker: C
text: But um ,
speaker: C
text: Good .
speaker: C
text: OK .
speaker: C
text: Anything else going on ?
speaker: C
text: at you guys ' end ,
speaker: C
text: or <disfmarker> ?
speaker: B
text: I don't have good result , with the <disfmarker> inc including the new parameters ,
speaker: B
text: I don't have good result .
speaker: B
text: Are <pause> similar or a little bit worse .
speaker: A
text: With what <disfmarker> what other new p new parameter ?
speaker: G
text: You 're talking about your voicing ?
speaker: C
text: Yeah .
speaker: C
text: So maybe <disfmarker> You probably need to back up a bit
speaker: B
text: Yeah .
speaker: B
text: Mm - hmm .
speaker: A
text: Yeah .
speaker: C
text: seeing as how Sunil ,
speaker: B
text: I tried to include another new parameter to the traditional parameter ,
speaker: C
text: yeah .
speaker: B
text: the coe the cepstrum coefficient ,
speaker: A
text: Uh - huh .
speaker: B
text: that , like , the auto - correlation , the R - zero and R - one over R - zero
speaker: A
text: Mm - hmm .
speaker: A
text: Mm - hmm .
speaker: B
text: and another estimation of the var the variance of the difference for <disfmarker> of the spec si uh , spectrum of the signal
speaker: B
text: and <disfmarker> and the spectrum of time after filt mel filter bank .
speaker: A
text: I 'm so sorry .
speaker: A
text: I didn't get it .
speaker: B
text: Nuh . Well .
speaker: B
text: Anyway .
speaker: B
text: The <disfmarker> First you have the sp the spectrum of the signal ,
speaker: A
text: Mm - hmm .
speaker: B
text: and you have the <disfmarker> on the other side you have the output of the mel filter bank .
speaker: A
text: Mm - hmm .
speaker: B
text: You can extend the coefficient of the mel filter bank and obtain an approximation of the spectrum of the signal .
speaker: A
text: Mmm .
speaker: A
text: OK .
speaker: B
text: I do the difference <disfmarker>
speaker: A
text: OK .
speaker: B
text: I found a difference at the variance of this different
speaker: A
text: Uh - huh .
speaker: B
text: because , suppose we <disfmarker> we think that if the variance is high , maybe you have n uh , noise .
speaker: A
text: Yeah .
speaker: B
text: And if the variance is small , maybe you have uh , speech .
speaker: A
text: Uh - huh .
speaker: B
text: To <disfmarker> to
speaker: B
text: To <disfmarker>
speaker: B
text: The idea is to found another feature for discriminate between voice sound and unvoice sound .
speaker: A
text: OK .
speaker: B
text: And we try to use this new feature <disfmarker> feature .
speaker: B
text: And I did experiment <disfmarker>
speaker: B
text: I need to change <disfmarker> to obtain this new feature I need to change the size <disfmarker> the window size <disfmarker> size .
speaker: B
text: of the a of the <disfmarker> analysis window size ,
speaker: B
text: to have more information .
speaker: A
text: Yeah .
speaker: A
text: Make it longer .
speaker: B
text: Uh , sixty - two point five milliseconds I think .
speaker: A
text: OK .
speaker: B
text: And
speaker: B
text: I do <disfmarker> I did two type of experiment to include this feature directly with the <disfmarker> with the other feature
speaker: B
text: and to train a neural network to select it voice - unvoice - silence <disfmarker> silence
speaker: A
text: Unvoiced .
speaker: A
text: Well .
speaker: B
text: and to <disfmarker> to concat this new feature .
speaker: B
text: But the result are
speaker: B
text: n with the neural network I have more or less the same result .
speaker: A
text: As using just the cepstrum ,
speaker: B
text: Result .
speaker: A
text: or <disfmarker> ?
speaker: B
text: Yeah .
speaker: B
text: Yeah .
speaker: A
text: OK .
speaker: B
text: It 's neve e e
speaker: B
text: sometime it 's worse ,
speaker: B
text: sometime it 's a little bit better ,
speaker: B
text: but not significantly .
speaker: B
text: And <disfmarker>
speaker: A
text: Uh , is it with TI - digits , or with <disfmarker> ?
speaker: B
text: No , I work with eh , Italian and Spanish basically .
speaker: A
text: OK .
speaker: A
text: OK .
speaker: B
text: And if I don't y use the neural network , and use directly the feature the results are worse .
speaker: A
text: Uh - huh .
speaker: B
text: But Doesn't help .
speaker: D
text: Mm - hmm .
speaker: C
text: I <disfmarker> I <disfmarker> I really wonder though .
speaker: C
text: I mean we 've had these discussions before ,
speaker: C
text: and <disfmarker> and one of the things that struck me was that <disfmarker> uh , about this line of thought that was particularly interesting to me was that we um <disfmarker> whenever you condense things , uh , in an irreversible way , um , you throw away some information .
speaker: C
text: And , that 's mostly viewed on as a good thing , in the way we use it ,
speaker: C
text: because we wanna suppress things that will cause variability for uh particular , uh , phonetic units .
speaker: C
text: Um ,
speaker: C
text: but , you 'll do throw something away .
speaker: C
text: And so the question is , uh , can we figure out if there 's something we 've thrown away that we shouldn't have .
speaker: C
text: And um .
speaker: C
text: So ,
speaker: C
text: when they were looking at the difference between the filter bank and the FFT that was going into the filter bank , I was thinking " oh , OK ,
speaker: C
text: so they 're picking on something
speaker: C
text: they 're looking on it to figure out noise , or voice <disfmarker> voiced property whatever . "
speaker: C
text: So that <disfmarker> that 's interesting .
speaker: C
text: Maybe that helps to drive the <disfmarker> the thought process of coming up with the features .
speaker: C
text: But for me sort of the interesting thing was , " well , but is there just something in that difference which is useful ? "
speaker: C
text: So another way of doing it , maybe , would be just to take the FFT uh , power spectrum , and feed it into a neural network ,
speaker: C
text: and then use it , you know , in combination , or alone , or <disfmarker> or whatever
speaker: B
text: To know <disfmarker>
speaker: F
text: Wi - with what targets ?
speaker: A
text: Voiced , unvoiced is like <disfmarker>
speaker: C
text: Uh , no .
speaker: A
text: Oh .
speaker: A
text: Or anything .
speaker: C
text: No
speaker: C
text: the <disfmarker> just the same <disfmarker> same way we 're using <disfmarker> I mean , the same way that we 're using the filter bank .
speaker: F
text: Phones .
speaker: A
text: Oh , OK .
speaker: D
text: Mm - hmm .
speaker: C
text: Exact way <disfmarker> the same way we 're using the filter bank .
speaker: C
text: I mean , the filter bank is good for all the reasons that we say it 's good .
speaker: C
text: But it 's different .
speaker: C
text: And , you know , maybe if it 's used in combination , it will get at something that we 're missing .
speaker: D
text: Mm - hmm .
speaker: C
text: And maybe , you know , using , orth you know , KLT , or uh , um , adding probabilities , I mean , all th all the different ways that we 've been playing with , that we would let the <disfmarker> essentially let the neural network determine what is it that 's useful , that we 're missing here .
speaker: D
text: Mm - hmm .
speaker: D
text: Yeah , but there is so much variability in the power spectrum .
speaker: A
text: Mm - hmm .
speaker: C
text: Well , that 's probably why y i it would be unlikely to work as well by itself ,
speaker: D
text: Mm - hmm .
speaker: C
text: but it might help in combination .
speaker: D
text: Mmm .
speaker: C
text: But I <disfmarker> I <disfmarker> I have to tell you ,
speaker: C
text: I can't remember the conference ,
speaker: C
text: but , uh , I think it 's about ten years ago , I remember going to one of the speech conferences
speaker: C
text: and <disfmarker> and uh ,
speaker: C
text: I saw within very short distance of one another a couple different posters that showed about the wonders of some auditory inspired front - end or something ,
speaker: C
text: and a couple posters away it was somebody who compared one to uh , just putting in the FFT
speaker: C
text: and the FFT did slightly better .
speaker: D
text: Mm - hmm .
speaker: C
text: So I mean the <disfmarker> i i It 's true there 's lots of variability ,
speaker: C
text: but again we have these wonderful statistical mechanisms for quantifying that a that variability , and you know , doing something reasonable with it .
speaker: D
text: Mm - hmm .
speaker: C
text: So ,
speaker: C
text: um ,
speaker: C
text: uh ,
speaker: C
text: It - it 's same , you know , argument that 's gone both ways about uh , you know , we have these data driven filters , in LDA ,
speaker: C
text: and on the other hand , if it 's data driven it means it 's driven by things that have lots of variability , and that are necessarily <disfmarker> not necessarily gonna be the same in training and test ,
speaker: C
text: so , in some ways it 's good to have data driven things ,
speaker: C
text: and in some ways it 's bad to have data driven things .
speaker: C
text: So ,
speaker: A
text: Yeah , d
speaker: C
text: part of what we 're discovering , is ways to combine things that are data driven than are not .
speaker: A
text: Yeah .
speaker: C
text: Uh , so anyway , it 's just a thought , that <disfmarker> that if we <disfmarker> if we had that <disfmarker> maybe it 's just a baseline
speaker: C
text: uh , which would show us " well , what are we really getting out of the filters " ,
speaker: C
text: or maybe i i
speaker: C
text: probably not by itself ,
speaker: C
text: but in combination ,
speaker: D
text: Mm - hmm .
speaker: C
text: uh ,
speaker: C
text: you know , maybe there 's something to be gained from it ,
speaker: C
text: and let the <disfmarker>
speaker: C
text: But , you know , y you 've only worked with us for a short time ,
speaker: C
text: maybe in a year or two you w you will actually come up with the right set of things to extract from this information .
speaker: C
text: But , maybe the neural net and the H M Ms could figure it out quicker than you .
speaker: B
text: Maybe .
speaker: C
text: So .
speaker: C
text: It 's just a thought .
speaker: B
text: Yeah ,
speaker: B
text: I can <disfmarker> I will try to do that .
speaker: C
text: Yeah .
speaker: A
text: What <disfmarker> one <disfmarker> one um p one thing is like what <disfmarker> before we started using this VAD in this Aurora , the <disfmarker> th what we did was like , I <disfmarker> I guess most of you know about this , adding this additional speech - silence bit to the cepstrum and training the HMM on that .
speaker: C
text: Mm - hmm .
speaker: A
text: That is just a binary feature
speaker: A
text: and that seems to be <vocalsound> improving a lot on the SpeechDat - Car where there is a lot of noise
speaker: A
text: but not much on the TI - digits .
speaker: A
text: So , a adding an additional feature to distin to discriminate between speech and nonspeech was helping .
speaker: A
text: That 's it .
speaker: D
text: Wait <disfmarker> I <disfmarker> I 'm sorry ?
speaker: A
text: Yeah , we actually added an additional binary feature to the cepstrum ,
speaker: A
text: just the baseline .
speaker: D
text: Yeah ?
speaker: B
text: You did some experiment .
speaker: A
text: Yeah ,
speaker: A
text: yeah .
speaker: A
text: Well , in <disfmarker> in the case of TI - digits it didn't actually give us anything ,
speaker: A
text: because there wasn't any f anything to discriminate between speech ,
speaker: D
text: Yeah .
speaker: A
text: and it was very short .
speaker: D
text: Hmm .
speaker: A
text: But Italian was like very <disfmarker> it was a huge improvement on Italian .
speaker: D
text: Well <disfmarker>
speaker: D
text: Mm - hmm .
speaker: D
text: But anyway the question is even more , is within speech , can we get some features ?
speaker: D
text: Are we drop dropping information that can might be useful within speech ,
speaker: D
text: I mean . To <disfmarker> maybe to distinguish between voice sound and unvoiced sounds ?
speaker: A
text: OK .
speaker: A
text: Mm - hmm .
speaker: A
text: Yeah , yeah .
speaker: A
text: Yeah .
speaker: C
text: And it 's particularly more relevant now since we 're gonna be given the endpoints .
speaker: D
text: Yeah .
speaker: D
text: Mm - hmm .
speaker: C
text: So .
speaker: A
text: Yeah , yeah .
speaker: C
text: Uh .
speaker: D
text: Mmm .
speaker: C
text: So .
speaker: C
text: Um .
speaker: A
text: Mmm .
speaker: A
text: There was a paper in ICASSP <disfmarker> this ICASSP <disfmarker> over the uh extracting some higher - order uh , information from the cepstral coefficients
speaker: A
text: and I forgot the name .
speaker: A
text: Some is some harmonics
speaker: A
text: I don't know ,
speaker: A
text: I can <disfmarker> I can pull that paper out from ICASSP .
speaker: D
text: Yeah .
speaker: C
text: Talking cumulants or something ?
speaker: A
text: It <disfmarker>
speaker: A
text: Huh ?
speaker: C
text: Cumulants or something .
speaker: A
text: Uh , I don't know .
speaker: C
text: But <disfmarker> No .
speaker: A
text: I don't remember .
speaker: A
text: It wa it was taking the , um <disfmarker>
speaker: A
text: It was about finding the higher - order moments of <disfmarker>
speaker: C
text: Yeah ,
speaker: A
text: Yeah .
speaker: C
text: cumulants ,
speaker: A
text: And I 'm not sure about whether it is the higher - order moments , or <disfmarker>
speaker: C
text: yeah .
speaker: A
text: maybe higher - order cumulants
speaker: C
text: Oh .
speaker: A
text: and <disfmarker>
speaker: A
text: Yeah .
speaker: C
text: Or m e
speaker: A
text: It was <disfmarker> it was <disfmarker>
speaker: A
text: Yeah .
speaker: A
text: I mean , he was showing up uh some <disfmarker> something on noisy speech ,
speaker: C
text: Yeah .
speaker: A
text: some improvement on the noisy speech .
speaker: D
text: Mm - hmm .
speaker: A
text: Some small vocabulary tasks .
speaker: C
text: Uh .
speaker: A
text: So it was on PLP derived cepstral coefficients .
speaker: C
text: Yeah , but again <disfmarker> You could argue that th that 's exactly what the neural network does .
speaker: A
text: Mmm .
speaker: C
text: So n neural network uh , is in some sense equivalent to computing , you know , higher - order moments of what you <disfmarker>
speaker: A
text: trying to f
speaker: A
text: to
speaker: A
text: Moments ,
speaker: A
text: yeah .
speaker: C
text: yeah .
speaker: A
text: Yeah .
speaker: C
text: So .
speaker: C
text: I mean , it doesn't do it very specifically ,
speaker: D
text: Mm - hmm .
speaker: C
text: and
speaker: C
text: pretty <disfmarker> you know .
speaker: C
text: But .
speaker: A
text: Yep .
speaker: C
text: Uh ,
speaker: C
text: anything on your end you want to talk about ?
speaker: C
text: Uh .
speaker: G
text: Um , nothing I wanna really talk about .
speaker: G
text: I can <disfmarker> I can just uh , um , share a little bit <disfmarker>
speaker: G
text: Sunil hasn't <disfmarker> hasn't heard about uh , what I 've been doing .
speaker: C
text: Yeah .
speaker: G
text: Um ,
speaker: G
text: so ,
speaker: G
text: um , I told you I was <disfmarker> I was <disfmarker> I was getting prepared to take this qualifier exam .
speaker: G
text: So basically that 's just , um , trying to propose um , uh , your next your <disfmarker> your following years of <disfmarker> of your PHD work ,
speaker: G
text: trying <disfmarker> trying to find a project to <disfmarker> to define and <disfmarker> and to work on .
speaker: G
text: So , I 've been , uh , looking into , um , doing something about r uh , speech recognition using acoustic events .
speaker: G
text: So ,
speaker: G
text: um , the idea is you have all these <disfmarker> these different events ,
speaker: G
text: for example voicing ,
speaker: G
text: nasality ,
speaker: G
text: R - coloring ,
speaker: G
text: you know burst
speaker: G
text: or noise , uh , frication ,
speaker: G
text: that kinda stuff ,
speaker: G
text: um , building robust um , primary detectors for these acoustic events ,
speaker: G
text: and using the outputs of these robust detectors to do speech recognition .
speaker: G
text: Um ,
speaker: G
text: and , um , these <disfmarker> these primary detectors , um , will be , uh , inspired by , you know , multi - band techniques , um , doing things , um , similar to Larry Saul 's work on , uh , graphical models to <disfmarker> to detect these <disfmarker> these , uh , acoustic events .
speaker: G
text: And , um , so I <disfmarker> I been <disfmarker> I been thinking about that
speaker: G
text: and some of the issues that I 've been running into are , um , exactly what <disfmarker> what kind of acoustic events I need ,
speaker: G
text: what <disfmarker> um , what acoustic events will provide a <disfmarker> a good enough coverage to <disfmarker> in order to do the later recognition steps .
speaker: G
text: And , also , um , once I decide a set of acoustic events , um , h how do I <disfmarker> how do I get labels ?
speaker: G
text: Training data for <disfmarker> for these acoustic events .
speaker: G
text: And , then later on down the line , I can start playing with the <disfmarker> the models themselves ,
speaker: G
text: the <disfmarker> the primary detectors .
speaker: G
text: Um ,
speaker: G
text: so ,
speaker: G
text: um , I kinda see <disfmarker> like , after <disfmarker> after building the primary detectors I see um , myself taking the outputs and feeding them in , sorta tandem style into <disfmarker> into a um , Gaussian mixtures HMM back - end , um , and doing recognition .
speaker: G
text: Um .
speaker: G
text: So , that 's <disfmarker> that 's just generally what I 've been looking at .
speaker: A
text: Yeah .
speaker: G
text: Um ,
speaker: C
text: By <disfmarker> by the way , uh , the voiced - unvoiced version of that for instance could tie right in to what Carmen was looking at .
speaker: G
text: Yeah .
speaker: C
text: So ,
speaker: D
text: Mm - hmm .
speaker: C
text: you know , um , if you <disfmarker> if a multi - band approach was helpful as <disfmarker> as I think it is ,
speaker: C
text: it seems to be helpful for determining voiced - unvoiced ,
speaker: G
text: Mm - hmm .
speaker: C
text: that one might be another thing .
speaker: G
text: Yeah .
speaker: B
text: Mm - hmm .
speaker: G
text: Yeah .
speaker: G
text: Um ,
speaker: G
text: were <disfmarker> were you gonna say something ?
speaker: F
text: Mmm .
speaker: G
text: Oh .
speaker: G
text: It looked <disfmarker>
speaker: G
text: OK ,
speaker: G
text: never mind .
speaker: G
text: Um , yeah .
speaker: G
text: And so , this <disfmarker> this past week um , I 've been uh , looking a little bit into uh , TRAPS um , and doing <disfmarker> doing TRAPS on <disfmarker> on these e events too ,
speaker: G
text: just , um , seeing <disfmarker> seeing if that 's possible .
speaker: G
text: Uh ,
speaker: G
text: and
speaker: G
text: um , other than that , uh , I was kicked out of I - house for living there for four years .
speaker: C
text: Oh no .
speaker: C
text: So you live in a cardboard box in the street now
speaker: G
text: Yeah .
speaker: C
text: or , no ?
speaker: G
text: Uh , well , s s som something like that .
speaker: C
text: Yeah .
speaker: G
text: In Albany ,
speaker: G
text: yeah .
speaker: G
text: Yeah .
speaker: G
text: And uh .
speaker: G
text: Yep .
speaker: G
text: That 's it .
speaker: C
text: Suni - i d ' you v did uh <disfmarker> did you find a place ?
speaker: C
text: Is that out of the way ?
speaker: A
text: Uh , no
speaker: A
text: not yet .
speaker: A
text: Uh , yesterday I called up a lady who ha who will have a vacant room from May thirtieth
speaker: A
text: and she said she 's interviewing two more people .
speaker: A
text: So .
speaker: A
text: And she would get back to me on Monday .
speaker: A
text: So that 's <disfmarker> that 's only thing I have
speaker: A
text: and Diane has a few more houses .
speaker: A
text: She 's going to take some pictures and send me after I go back .
speaker: C
text: OK .
speaker: A
text: So it 's <disfmarker> that 's <disfmarker>
speaker: F
text: Oh .
speaker: F
text: So you 're not down here permanently yet ?
speaker: A
text: No .
speaker: A
text: I 'm going back to OGI today .
speaker: F
text: Ah !
speaker: F
text: Oh , OK .
speaker: G
text: Oh .
speaker: C
text: OK .
speaker: C
text: And then , you 're coming back uh <disfmarker>
speaker: A
text: Uh , i I mean , I <disfmarker> I p I plan to be here on thirty - first .
speaker: C
text: Thirty - first ,
speaker: C
text: OK .
speaker: G
text: Thirty - first .
speaker: A
text: Yeah ,
speaker: A
text: well if there 's a house available or place to <disfmarker>
speaker: C
text: Well , I mean i i if <disfmarker> if <disfmarker>
speaker: A
text: Yeah , I hope .
speaker: C
text: They 're available ,
speaker: C
text: and they 'll be able to get you something ,
speaker: C
text: so worst comes to worst we 'll put you up in a hotel for <disfmarker> for <disfmarker> for a while
speaker: A
text: Yeah .
speaker: A
text: So , in that case , I 'm going to be here on thirty - first definitely .
speaker: C
text: until you <disfmarker>
speaker: C
text: OK .
speaker: E
text: You know , if you 're in a desperate situation and you need a place to stay , you could stay with me for a while .
speaker: E
text: I 've got a spare bedroom right now .
speaker: A
text: Oh . OK .
speaker: A
text: Thanks .
speaker: A
text: That sure is nice of you .
speaker: A
text: So , it may be he needs more than me .
speaker: G
text: Oh r
speaker: G
text: oh .
speaker: G
text: Oh no , no .
speaker: G
text: My <disfmarker> my cardboard box is actually a nice spacious two bedroom apartment .
speaker: C
text: So a two bedroom cardboard box .
speaker: C
text: Th - that 's great .
speaker: G
text: yeah
speaker: A
text: Yeah . Yeah . <vocalsound> Yeah .
speaker: C
text: Thanks Dave .
speaker: A
text: Yeah .
speaker: C
text: Um ,
speaker: A
text: Yeah .
speaker: C
text: Do y wanna say anything about <disfmarker>
speaker: C
text: You <disfmarker> you actually been <disfmarker>
speaker: C
text: Uh , last week you were doing this stuff with Pierre , you were <disfmarker> you were mentioning .
speaker: C
text: Is that <disfmarker> that something worth talking about ,
speaker: C
text: or <disfmarker> ?
speaker: E
text: Um ,
speaker: E
text: it 's <disfmarker>
speaker: E
text: Well , um , it <disfmarker> I don't think it directly relates .
speaker: E
text: Um , well , so , I was helping a speech researcher named Pierre Divenyi
speaker: E
text: and he 's int He wanted to um , look at um , how people respond to formant changes , I think .
speaker: E
text: Um .
speaker: E
text: So he <disfmarker> he created a lot of synthetic audio files of vowel - to - vowel transitions ,
speaker: E
text: and then he wanted a psycho - acoustic um , spectrum .
speaker: E
text: And he wanted to look at um , how the energy is moving <pause> over time in that spectrum
speaker: E
text: and compare that to the <disfmarker> to the listener tests .
speaker: E
text: And , um .
speaker: E
text: So , I gave him a PLP spectrum .
speaker: E
text: And <disfmarker> to um <disfmarker> he <disfmarker> he t wanted to track the peaks so he could look at how they 're moving .
speaker: E
text: So I took the um , PLP LPC coefficients and um , I found the roots .
speaker: E
text: This was something that Stephane suggested .
speaker: E
text: I found the roots of the um , LPC polynomial to , um , track the peaks in the , um , PLP LPC spectra .
speaker: A
text: well there is aligned spectral pairs , is like the <disfmarker> the <disfmarker>
speaker: A
text: Is that the aligned s
speaker: C
text: It 's a r root LPC , uh , of some sort .
speaker: A
text: Oh , no .
speaker: D
text: Mm - hmm .
speaker: A
text: So you just <disfmarker>
speaker: C
text: Yeah .
speaker: A
text: instead of the log you took the root square , I mean cubic root or something .
speaker: A
text: What di w I didn't get that .
speaker: C
text: No , no .
speaker: C
text: It 's <disfmarker> it 's <disfmarker> it 's taking the <disfmarker> finding the roots of the LPC polynomial .
speaker: A
text: Polynomial .
speaker: A
text: Yeah .
speaker: A
text: Is that the line spectral <disfmarker>
speaker: C
text: So it 's like line spectral pairs .
speaker: A
text: Oh , it 's like line sp
speaker: C
text: Except I think what they call line spectral pairs they push it towards the unit circle ,
speaker: C
text: don't they ,
speaker: C
text: to sort of ?
speaker: A
text: Yeah , yeah , yeah , yeah .
speaker: C
text: But it <disfmarker>
speaker: C
text: But uh , you know .
speaker: C
text: But what we 'd used to do w when I did synthesis at National Semiconductor twenty years ago , the technique we were playing with initially was <disfmarker> was taking the LPC polynomial and <disfmarker> and uh , finding the roots .
speaker: C
text: It wasn't PLP cuz Hynek hadn't invented it yet ,
speaker: C
text: but it was just LPC ,
speaker: C
text: and uh , we found the roots of the polynomial ,
speaker: C
text: And th When you do that , sometimes they 're f they 're what most people call formants ,
speaker: C
text: sometimes they 're not .
speaker: A
text: Mmm .
speaker: C
text: So it 's <disfmarker> it 's <disfmarker> it 's a little ,
speaker: D
text: Hmm .
speaker: C
text: uh <disfmarker> Formant tracking with it can be a little tricky
speaker: C
text: cuz you get these funny <vocalsound> values in <disfmarker> in real speech ,
speaker: F
text: So you just <disfmarker> You typically just get a few roots ?
speaker: C
text: but .
speaker: F
text: You know ,
speaker: F
text: two or three ,
speaker: F
text: something like that ?
speaker: C
text: Well you get these complex pairs .
speaker: C
text: And it depends on the order that you 're doing ,
speaker: D
text: Mm - hmm .
speaker: E
text: Right .
speaker: C
text: but .
speaker: E
text: So , um ,
speaker: E
text: if <disfmarker> @ @ <comment> Every root that 's <disfmarker>
speaker: E
text: Since it 's a real signal , the LPC polynomial 's gonna have real coefficients .
speaker: E
text: So I think that means that every root that is not a real root <comment> is gonna be a c complex pair ,
speaker: F
text: Mm - hmm .
speaker: E
text: um , of a complex value and its conjugate .
speaker: E
text: Um .
speaker: E
text: So for each <disfmarker>
speaker: E
text: And if you look at that on the unit circle , um , one of these <disfmarker> one of the members of the pair will be a positive frequency , one will be a negative frequency , I think .
speaker: E
text: So I just <disfmarker>
speaker: E
text: So , um ,
speaker: E
text: f for the <disfmarker> I 'm using an eighth - order polynomial
speaker: E
text: and I 'll get three or four of these pairs
speaker: C
text: Yeah .
speaker: E
text: which give me s which gives me three or four peak positions .
speaker: A
text: Hmm .
speaker: C
text: This is from synthetic speech ,
speaker: C
text: or <disfmarker> ?
speaker: E
text: It 's <disfmarker> Right .
speaker: E
text: Yeah .
speaker: C
text: Yeah .
speaker: C
text: So if it 's from synthetic speech then maybe it 'll be cleaner .
speaker: C
text: I mean for real speech in real <disfmarker> then what you end up having is , like I say , funny little things that are <disfmarker> don't exactly fit your notion of formants all that well .
speaker: F
text: How did <disfmarker>
speaker: D
text: But
speaker: C
text: But <disfmarker> but mostly they are .
speaker: D
text: Yeah .
speaker: C
text: Mostly they do .
speaker: E
text: Mmm ,
speaker: C
text: And <disfmarker> and what <disfmarker> I mean in <disfmarker> in what we were doing , which was not so much looking at things , it was OK
speaker: D
text: I <disfmarker>
speaker: C
text: because it was just a question of quantization .
speaker: C
text: Uh , we were just you know , storing <disfmarker>
speaker: C
text: It was <disfmarker> We were doing , uh , stored speech , uh , quantization .
speaker: D
text: Mm - hmm .
speaker: C
text: But <disfmarker> but uh , in your case
speaker: C
text: um , you know <disfmarker>
speaker: D
text: Actually you have peaks that are not at the formant 's positions ,
speaker: D
text: but they are lower in energy
speaker: E
text: But <disfmarker> there 's some of that ,
speaker: E
text: yes .
speaker: D
text: and <disfmarker> Well they are much lower .
speaker: F
text: If this is synthetic speech can't you just get the formants directly ?
speaker: F
text: I mean h how is the speech created ?
speaker: E
text: It was created from a synthesizer ,
speaker: E
text: and um <disfmarker>
speaker: F
text: Wasn't a formant synthesizer was it ?
speaker: C
text: I bet it <disfmarker> it might have <disfmarker> may have been
speaker: E
text: I <disfmarker> d d this <disfmarker>
speaker: C
text: but maybe he didn't have control over it or something ?
speaker: E
text: In <disfmarker> in fact w we <disfmarker> we could get , um , formant frequencies out of the synthesizer , as well .
speaker: E
text: And , um ,
speaker: E
text: w one thing that the , um , LPC approach will hopefully give me in addition , um , is that I <disfmarker> I might be able to find the b the bandwidths of these humps as well .
speaker: E
text: Um , Stephane suggested looking at each complex pair as a <disfmarker> like a se second - order IIR filter .
speaker: C
text: Yeah .
speaker: E
text: Um ,
speaker: E
text: but I don't think there 's a g a really good reason not to um , get the formant frequencies from the synthesizer instead .
speaker: E
text: Except that you don't have the psycho - acoustic modeling in that .
speaker: C
text: Yeah ,
speaker: C
text: so the actual <disfmarker>
speaker: C
text: So you 're not getting the actual formants per se .
speaker: C
text: You 're getting the <disfmarker> Again , you 're getting sort of the ,
speaker: C
text: uh <disfmarker>
speaker: D
text: Mm - hmm .
speaker: C
text: You 're getting something that is <disfmarker> is uh , af strongly affected by the PLP model .
speaker: C
text: And so it 's more psycho - acoustic .
speaker: C
text: So it 's a little <disfmarker> It 's <disfmarker> It 's <disfmarker> It 's sort of <disfmarker> sort of a different thing .
speaker: F
text: Oh ,
speaker: F
text: I see .
speaker: F
text: That 's sort of the point .
speaker: C
text: But <disfmarker> Yeah .
speaker: C
text: i Ordinarily , in a formant synthesizer , the bandwidths as well as the ban uh , formant centers are <disfmarker>
speaker: F
text: Yeah .
speaker: C
text: I mean , that 's <disfmarker> Somewhere in the synthesizer that was put in ,
speaker: C
text: as <disfmarker> as what you <disfmarker>
speaker: E
text: Mm - hmm .
speaker: C
text: But <disfmarker> but yeah ,
speaker: C
text: you view each complex pair as essentially a second - order section ,
speaker: C
text: which has , uh , band center and band width ,
speaker: C
text: and um ,
speaker: C
text: um <disfmarker>
speaker: C
text: But .
speaker: C
text: Yeah .
speaker: C
text: O K .
speaker: C
text: So , uh ,
speaker: C
text: yeah , you 're going back today
speaker: C
text: and then back in a week I guess ,
speaker: C
text: and .
speaker: A
text: Yeah .
speaker: C
text: Yeah .
speaker: C
text: Great !
speaker: C
text: Well , welcome .
speaker: A
text: Thanks .
speaker: F
text: I guess we should do digits quickly .
speaker: D
text: Mmm .
speaker: C
text: Oh yeah , digits .
speaker: C
text: I almost forgot that .
speaker: B
text: Digits .
speaker: C
text: I almost forgot our daily digits .
speaker: F
text: You wanna go ahead ?
speaker: C
text: Sure .
speaker: F
text: OK .

Meeting summary:
text: The ICSI Meeting Recorder Group at Berkley have a temporary new member on loan from research partner OGI.
text: He began the meeting by reporting his recent activities, which included looking at the new baseline system.
text: The other members of the group also reported their recent progress in areas such as spectral subtraction and voicing detection.
text: They also explained some of their projects to their guest.
text: The group shall soon be taking delivery of more machines for a computation farm, and they discussed some software tools for running large processes.
text: Speaker me018 will construct an FAQ about the new computing tools and setup, and email details.
text: Fn002 agrees to try an alternative approach to her new feature for voicing detection.
text: Speaker mn007 has taken the spectral subtraction from another groups system, and is trying it with their own, with mixed results.
text: He is also looking into alternative methods of removing noise.
text: Fn002 is still working on voicing detection, and has run experiments with her new feature with disappointing results.
text: Though not directly related to the groups work, speaker me006 has been putting together his proposal or a PhD, and me026 has been helping another researcher with his work on formants.