yeah because i didn't


for the france telecom uh spectral subtraction included in the our system the t.i. digits number are the right one.


but not for the other system.


because i didn't test it yet this system including with spectral subtraction on the t.i. digits data.


i just tested it on speechdat-car.


uhhuh.


uh!


so so that means the only thing


so so so these numbers are simply


this we have to


yeah.


but this number.


okay.


yes.


so you so you just should look at that fifty eight point o. nine percent and so on.


right.


good.


um


so this


so by uh by by reducing the noise a a decent threshold like minus thirty d.b. it's like uh you are like reducing the floor of the noisy regions right?


the floor is lower.


i'm sorry.


so when you say minus twenty five or minus thirty d.b. with respect to what?


to the average um speech energy.


which is estimated on the world database.


so basically you're creating a signal to noise ratio of twenty five or thirty d.b.


but it's not


i i i think what you do is this.


uh


it it's


when when you have this after you subtracted it i mean then you get something with this uh where you set the values to zero.


and then you simply add an additive constant again.


yeah.


so you shift it somehow.


this this whole curve is shifted again.


right.


but did you do that before the thresholding to zero?


it's


or


but it's after the thresholding.


so


oh.


so you'd really want to do it before.


maybe


right?


maybe we might do it before.


because then the then you would have less of that phenomenon.


hhh.


i think.


uh


yeah.


but still when you do this and you take the log after that it it reduce the the variance.


it it


but


right.


huh


yeah that will reduce the variance.


that'll help.


but maybe if you does do it before you get less of these funny looking things he's drawing.


uhhuh.


um


so before it's like adding this to the to the original.


but but


we would


right at the point where you've done the subtraction.


okay.


um essentially you're adding a constant into everything.


but the way stephane did it it is exactly the way i have implemented in the phone.


so


oh yeah.


better do it different then.


yeah.


just you you just you just set it for a particular signal to noise ratio that you want.


yeah i i made similar investigations like stephane did here.


just uh adding this constant and and looking how dependent is it on the value of the constant.


and then must choose them somehow to give on average the best results for a certain range of the signal to noise ratios.


uhhuh.


oh it's clear.


i should have given other results.


also it's clear when you don't add noise it's much worse.


like around five percent worse i guess.


and if you add too much noise it get worse also.


and it seems that right now this this is a constant that does not depend on on anything that you can learn from the utterance.


it's just a constant noise addition.


um


and i i think


i i'm sorry.


i think


then then i'm confused.


i thought


you're saying it doesn't depend on the utterance.


but i thought you were adding an amount that was twenty five d.b. down from the signal energy.


yeah.


so the way i did that i just measured the average speech energy of the all the italian data.


oh!


and then i i have i used this as mean speech energy.


uhhuh.


oh it's just a constant amount over all.


and


what i observed is that for italian and spanish when you go to thirty and twenty five d.b. uh it it's good.


okay.


oh.


it stays in this range.


it's uh the


well the performance of the this algorithm is quite good.


but for finnish you have a degradation already when you go from thirty five to thirty.


and then from thirty to twenty five.


and i have the feeling that maybe it's because just finnish has a mean energy that's lower than than the other databases.


and due to this the thresholds should be


yeah.


the the the noise addition should be lower.


but in i mean in the real thing you're not going to be able to measure what people are doing over half an hour or an hour or anything right?


so you have to come up with this number from something else.


so


uh but you are not doing it now language dependent?


or


it's not.


it's just something that's fixed.


no.


it's overall.


okay.


uhhuh.


um


yeah so i


but what he is doing language dependent is measuring what that number reference is that he comes down twenty five down from.


it no.


because i did it i started working on italian.


no?


i obtained this average energy.


yeah.


and then i used this one.


for all the languages.


so it's sort of arbitrary.


i mean so if if


yep.


yeah so the next thing is to use this as as maybe initialization.


and then use something online.


something more adaptive.


but and i expect improvement at least in finnish because uh the way


okay.


well.


um


for italian and spanish it's this value works good but not necessarily for finnish.


huh


but unfortunately there is like this forty millisecond latency.


and


yeah so i would try to somewhat reduce this.


i already know that if i completely remove this latency so um it um there is a three percent hit on italian.


uhhuh.


does latency


sorry.


go ahead.


yeah.


your your smoothing was uh over this so to say the the factor of the wiener.


and then it's uh


what was it?


this


this smoothing it was over the subtraction factor so to say.


it's a smoothing over the the gain of the subtraction algorithm.


was this done


and and you are looking into the future into the past.


right.


and smoothing.


so to smooth this thing.


um


and did did you try simply to smooth um to smooth the the to to smooth stronger the the envelope?


no.


i did not.


huh.


because i mean it should have a similar effect if you


yeah.


i mean you you have now several stages of smoothing so to say.


you start up.


as far as i remember you you smooth somehow the envelope.


you smooth somehow the noise estimate.


uhhuh.


and and later on you smooth also this subtraction factor.


uh no.


it's it's just the gain that's smoothed actually.


uh actually i i do all the smoothing.


but it's smoothed


uh.


oh it it was you.


yeah yeah.


uh yeah.


no in this case it's just the gain.


and


but the way it's done is that um for low gain there is this nonlinear smoothing actually.


for low gains um i use the smoothed uh smoothed version.


but for high gain it's i don't smooth.


i just uh it experience shows you if if you do the


the best is to do the smoothing as early as possible.


uhhuh.


so when you start up


i mean you start up with the with the somehow with the noisy envelope.


and best is to smooth this somehow.


i could try this.


um


so before estimating the s.n.r. smooth the envelope.


yeah.


but


then i i would need to find a way to like smooth less also when there is high energy.


because i noticed that it it helps a little bit to like smooth more during low energy portions and less during speech.


yes.


because if you smooth then you kind of distort the speech.


right.


yeah i think when


you you could do it in this way that you say if you if i'm


you have somehow a noise estimate.


uhhuh.


and if you say i'm i'm with my envelope.


i'm close to this noise estimate.


and then you you would like to have a stronger smoothing.


so you could you could base it on your estimation of the signal to noise ratio on your actual


huh.


or some silence probability from the vad if you have


um


yeah.


but i don't trust the current vad.


so


uh so not not right now maybe.


well maybe.


maybe.


the vad later will be much better.


i see.


so is that it?


uh fff i think that's it.


uh


so to summarize the performance of these speechdat-car results is similar than than yours so to say.


so the fifty eight is like the some fifty six point.


you have you have fifty six point four.


and and and dependent on this additive constant it is better or or worse.


that's true.


slightly better?


uhhuh.


yeah.


and yeah.


the condition where it's better than your approach it's it just because maybe it's better on well matched and that the weight on well matched is is bigger.


yeah you you caught up.


because


yep.


if you don't weigh differently the different condition you can see that your well the the two stage wiener filtering is maybe better.


or


it's better for high mismatch right?


it's better for high mismatch.


but a little bit worse for well matched.


so over all it gets yeah worse for the well matched condition.


so


so we need to combine these two.


uh


that's that's the best thing is like the french telecom system is optimized for the well matched condition.


uhhuh.


they


yeah.


so they know that the weighting is good for the well matched.


and so there's


everywhere the well matched's performance is very good for the french telecom.


we are we may also have to do something similar.


well our tradition here has always been to focus on the mismatched.


um the


because it's more interesting.


my mine was it too i mean.


before i started working on this aurora.


okay.


carmen?


do you uh


well i only say that the this is a summary of the of all the v.t.s. experiments.


and say that the result in the last um for italian the last experiment for italian are bad.


i make a mistake when i write.


up at d. i copy one of the bad result.


so you


and


there you know this


um


well.


if we put everything we improve a lot the spectral use of the v.t.s.


but the final result are not still huh good like the wiener filter for example.


i don't know.


maybe it's it's possible to to have the same result.


that's somewhere.


i don't know exactly.


huh.


because i have huh worse result in medium mismatch and high mismatch.


you you have a better


yeah.


you have some results that are good for the high mismatch.


and yeah.


i are more or less similar but but are worse.


and


still i don't have the result for t.i. digits.


the program is training.


maybe for this weekend i will have result t.i. digits.


and i can complete that like this.


well.


uh.


right.


one thing that i note are not here in this result but are speak are spoken before with sunil i i improve my result using clean l.d.a. filter.


uhhuh.


if i use uh the l.d.a. filter that are training with the noisy speech that hurts the my results.


so what are these numbers here?


are these with the clean or with the noisy?


this is with the clean.


okay.


with the noise i have worse result that if i doesn't use it.


but that may be because with this technique we are using really really clean speech.


the speech the representation that go to the h.t.k. is really clean speech.


because it's from the dictionary.


the code book.


and maybe from that.


i don't know.


because i think that you did some experiments using the two the two l.d.a. filter clean and and noise.


it's


and it doesn't matter too much.


um


yeah i did that.


but it doesn't matter on speechdat-car.


but it matters uh a lot on t.i. digits.


it's better to use clean.


using the clean filter.


yeah.


uh it's much better when you we used the clean derived l.d.a. filter.


uhhuh.


maybe you can do also this.


to use clean speech.


uh


yeah i'll try.


but yeah.


sunil in in your result it's


i i'll try the


no i i my result is with the noisy noisy l.d.a.


it's with the noisy one.


oh.


it's with the noisy.


so


it's it's not the clean l.d.a.


um


it's in in the front sheet i have like like the summary.


yeah.


and and your result is with the


it's with the clean l.d.a.


this is your results are all with the clean l.d.a. result.


yeah with the clean l.d.a.


okay.


is that the reason?


and in your case it's all all noisy.


all noisy.


but


and


uh


but i observe my case it's in uh uh at least on speechdat-car it doesn't matter.


but t.i. digits it's like two or three percent absolute uh better.


on t.i. digits this matters.


absolute.


so if


so you really might want to try the clean i think.


yeah i i i will have to look at it.


yeah that's true.


yeah.


yeah that could be sizeable right there.


and this is everything.


maybe you you are leaving in in about two weeks carmen.


okay.


no?


so i mean if if if i would put it put on the head of a project manager i i i would say uh um i mean there is not so much time left now.


be my guest.


i mean if um


what what i would do is i i i would pick the best consolation which you think.


and create create all the results for the whole database that you get to the final number as as sunil did it.


and prepare at the


and um


and maybe also to to write somehow a document where you describe your approach and what you have done.


i was thinking to do that next week.


i'll i'll borrow the head back and and agree.


i i i will do that next week.


yeah.


that's that's


right.


in fact actually i i guess the uh the spanish government uh requires that anyway.


they want some kind of report from everybody who's in the program.


uhhuh.


so


and of course i'd we'd we'd like to see it too.


okay.


so um


what's do you think we uh should do the digits or skip it?


or what are what do you think?


uh we have them now?


got them.


uh why why don't we do it?


just just take a minute.


i can send


yet.


would you pass those down?


oh.


sorry.


um


so i guess i'll go ahead.