Professor C: you know , stationary PhD A: Very slow adaptation .
PhD B: and is the c
PhD A: th
Professor C: Right , the word " stationary " is has a very precise statistical meaning . But , you know , in in signal - processing really what we 're talking about I think is things that change slowly , uh , compared with our our processing techniques .
PhD B: Mm - hmm .
Professor C: So if you 're driving along in a car I I would think that most of the time the nature of the noise is going to change relatively slowly . It 's not gonna stay absolute the same . If you if you check it out , uh , five minutes later you may be in a different part of the road
PhD B: Mm - hmm .
Professor C: or whatever . But it 's it 's i i i using the local characteristics in time , is probably going to work pretty well .
PhD B: Mm - hmm .
Professor C: But you could get hurt a lot if you just took some something from the beginning of all the speech , of , you know , an hour of speech and then later PhD B: Yeah .
Professor C: Uh , so they may be you know , may be overly , uh , complicated for for this test but but but , uh , I don't know . But what you 're saying , you know , makes sense , though . I mean , if possible you shouldn't you should you should make it , uh , the center of the center of the window . But uh , we 're already having problems with these delay , uh  delay issues .
PhD B: Yeah , so .
Professor C: So , uh , we 'll have to figure ways without it . Um ,
PhD A: If they 're going to provide a , uh , voice activity detector that will tell you the boundaries of the speech , then , couldn't you just go outside those boundaries and do your estimate there ?
Professor C: Oh , yeah . You bet . Yeah . So I I imagine that 's what they 're doing , right ? Is they 're they 're probably looking in nonspeech sections and getting some , uh PhD B: Yeah , they have some kind of threshold on on the previous estimate , and So . Yeah . I think . Yeah , I think Ericsson used this kind of threshold . Yeah , so , they h they have an estimate of the noise level and they put a threshold like six or ten DB above , and what 's under this threshold is used to update the estimate . Is is that right
PhD D: Yeah .
PhD B: or ?
PhD D: I think so .
PhD B: So it 's it 's PhD D: I have not here the proposal .
PhD B: Yeah . It 's like saying what 's under the threshold is silence ,
Professor C: Does France Telecom do this PhD B: and Grad E: Hmm .
Professor C: Does France Telecom do th do the same thing ? More or less ?
PhD B: I d I Y you know , perhaps ?
PhD D: No . I do I have not here the proposal .
Professor C: OK . Um , OK , if we 're we 're done done with that , uh , let 's see . Uh , maybe we can talk about a couple other things briefly , just , uh , things that that we 've been chatting about but haven't made it into these meetings yet . So you 're coming up with your quals proposal , and , uh Wanna just give a two three minute summary of what you 're planning on doing ?
Grad E: Oh , um , two , three , it can be shorter than that .
Professor C: Yeah .
Grad E: Um . Well , I 've I 've talked to some of you already . Um , but I 'm , uh , looking into extending the work done by Larry Saul and John Allen and uh Mazin Rahim . Um , they they have a system that 's , uh , a multi - band , um , system but their multi - band is is a little different than the way that we 've been doing multi - band in the past , where um Where we 've been @ @ {comment} uh taking um  sub - band features and i training up these neural nets and on on phonetic targets , and then combining them some somehow down the line , um , they 're they 're taking sub - band features and , um , training up a detector that detects for , um , these phonetic features for example , um , he presents um , uh , a detector to detect sonorance . And so what what it basically is is , um it 's there 's at the lowest level , there it 's it 's an OR ga I mean , it 's an AND gate . So , uh , on each sub - band you have several independent tests , to test whether um , there 's the existence of sonorance in a sub - band . And then , um , it c it 's combined by a soft AND gate . And at the at the higher level , for every if , um The higher level there 's a soft OR gate . Uh , so if if this detector detects um , the presence of of sonorance in any of the sub - bands , then the detect uh , the OR gate at the top says , " OK , well this frame has evidence of sonorance . "
PhD A: What are what are some of the low level detectors that they use ?
Grad E: And these are all Oh , OK . Well , the low level detectors are logistic regressions . Um , and the , uh Professor C: So that , by the way , basically is a is one of the units in our in our our neural network .
Grad E: the one o
Professor C: So that 's all it is . It 's a sig it 's a sigmoid ,
Grad E: Yeah .
Professor C: uh , with weighted sum at the input ,
PhD A: Hmm .
Professor C: which you train by gradient descent .
Grad E: Right . Yeah , so he uses , um , an EM algorithm to to um train up these um parameters for the logistic regression .
Professor C: Well , actually , yeah ,
Grad E: The Professor C: so I was using EM to get the targets . So so you have this this this AND gate what we were calling an AND gate , but it 's a product product rule thing at the output . And then he uses , uh , i u and then feeding into that are I 'm sorry , there 's it 's an OR at the output , isn't it ? Yeah ,
Grad E: Mm - hmm .
Professor C: so that 's the product . And then , um , then he has each of these AND things . And , um , but so they 're little neural neural units . Um , and , um , they have to have targets . And so the targets come from EM .
PhD A: And so are each of these , low level detectors {comment} are they , uh are these something that you decide ahead of time , like " I 'm going to look for this particular feature or I 'm going to look at this frequency , " or What what what are they looking at ?
Grad E: Um PhD A: What are their inputs ?
Grad E: Uh Right , so the OK , so at each for each sub - band {comment} there are basically , uh , several measures of SNR and and correlation .
PhD A: Ah , OK , OK .
Grad E: Um , um and he said there 's like twenty of these per per sub - band . Um , and for for every s every sub - band , e you you just pick ahead of time , um , " I 'm going to have like five i independent logistic tests . "
PhD A: Mm - hmm .
Grad E: And you initialize these parameters , um , in some some way and use EM to come up with your training targets for a for the the low - level detectors .
PhD A: Mm - hmm .
Grad E: And then , once you get that done , you you you train the whole whole thing on maximum likelihood . Um , and h he shows that using this this method to detect sonorance is it 's very robust compared to , um  to typical , uh , full - band Gaussian mixtures um estimations of of sonorance .
PhD A: Mm - hmm . Mm - hmm .
Grad E: And , uh so so that 's just that 's just one detector . So you can imagine building many of these detectors on different features . You get enough of these detectors together , um , then you have enough information to do , um , higher level discrimination , for example , discriminating between phones
PhD A: Mm - hmm .
Grad E: and then you keep working your way up until you you build a full recognizer .
PhD A: Mm - hmm .
Grad E: So , um , that 's that 's the direction which I 'm I 'm thinking about going in my quals .
PhD A: Cool .
Professor C: You know , it has a number of properties that I really liked . I mean , one is the going towards , um , using narrow band information for , uh , ph phonetic features of some sort rather than just , uh , immediately going for the the typical sound units .
PhD A: Right .
Professor C: Another thing I like about it is that you t this thing is going to be trained explicitly trained for a product of errors rule , which is what , uh , Allen keeps pointing out that Fletcher observed in the twenties ,
PhD A: Mm - hmm .
Professor C: uh , for people listening to narrow band stuff . That 's Friday 's talk , by the way . And then , um , Uh , the third thing I like about it is , uh , and we 've played around with this in a different kind of way a little bit but it hasn't been our dominant way of of operating anything , um , this issue of where the targets come from . So in our case when we 've been training it multi - band things , the way we get the targets for the individual bands is , uh , that we get the phonetic label for the sound there
PhD A: Mm - hmm .
Professor C: and we say , " OK , we train every " What this is saying is , OK , that 's maybe what our ultimate goal is or not ultimate but penultimate goal is getting these these small sound units . But but , um , along the way how much should we , uh uh , what should we be training these intermediate things for ? I mean , because , uh , we don't know uh , that this is a particularly good feature . I mean , there 's no way , uh someone in the audience yesterday was asking , " well couldn't you have people go through and mark the individual bands and say where the where it was sonorant or not ? "
PhD A: Mm - hmm .
Professor C: But , you know , I think having a bunch of people listening to critical band wide , uh , chunks of speech trying to determine whether {comment} I think it 'd be impossible .
Grad E: Ouch .