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false | Two hundred and fifty thousand. | QMSum_120 |
false | Fifteen hundred. Because Yeah. | QMSum_120 |
false | Yeah. Two thousand and fifteen hundred. | QMSum_120 |
false | Above , um it seems that Well , some voiced sound can have also , like , a noisy part on high frequencies , and But | QMSum_120 |
false | Yeah. | QMSum_120 |
false | Well , it 's just | QMSum_120 |
false | No , it 's makes sense to look at low frequencies. | QMSum_120 |
false | So this is uh , basically this is comparing an original version of the signal to a smoothed version of the same signal ? | QMSum_120 |
false | Yeah. | QMSum_120 |
false | Right. So i so i i this is I mean , i you could argue about whether it should be linear interpolation or or or or zeroeth order , but but | QMSum_120 |
false | Uh - huh. | QMSum_120 |
false | at any rate something like this is what you 're feeding your recognizer , typically. | QMSum_120 |
false | Like which of the ? | QMSum_120 |
false | No. Uh , so the mel cepstrum is the is the is the cepstrum of this this , uh , spectrum or log spectrum , | QMSum_120 |
false | So this is Yeah. | QMSum_120 |
false | Yeah. Right , right. | QMSum_120 |
false | whatever it You - you 're subtracting in in in power domain or log domain ? | QMSum_120 |
false | In log domain. Yeah. | QMSum_120 |
false | Log domain. | QMSum_120 |
false | OK. So it 's sort of like division , when you do the yeah , the spectra. | QMSum_120 |
false | Yeah. | QMSum_120 |
false | Uh , yeah. | QMSum_120 |
false | It 's the ratio. | QMSum_120 |
false | Um. Yeah. But , anyway , um and that 's | QMSum_120 |
false | So what 's th uh , what 's the intuition behind this kind of a thing ? I I don't know really know the signal - processing well enough to understand what what is that doing. | QMSum_120 |
false | So. Yeah. What happen if what we have have what we would like to have is some spectrum of the excitation signal , | QMSum_120 |
false | Yeah. I guess that makes sense. Yeah. | QMSum_120 |
false | which is for voiced sound ideally a a pulse train | QMSum_120 |
false | Uh - huh. | QMSum_120 |
false | and for unvoiced it 's something that 's more flat. | QMSum_120 |
false | Uh - huh. Right. | QMSum_120 |
false | And the way to do this is that well , we have the we have the FFT because it 's computed in in the in the system , and we have the mel filter banks , | QMSum_120 |
false | Mm - hmm. Mm - hmm. | QMSum_120 |
false | and so if we if we , like , remove the mel filter bank from the FFT , we have something that 's close to the excitation signal. | QMSum_120 |
false | Oh. | QMSum_120 |
false | It 's something that 's like a a a train of p a pulse train for voiced sound | QMSum_120 |
false | OK. | QMSum_120 |
false | Yeah. | QMSum_120 |
false | Oh ! OK. Yeah. | QMSum_120 |
false | and that 's that should be flat for | QMSum_120 |
false | Yeah. | QMSum_120 |
false | I see. So do you have a picture that sh ? | QMSum_120 |
false | So - It 's Y | QMSum_120 |
false | Is this for a voiced segment , | QMSum_120 |
false | yeah. | QMSum_120 |
false | this picture ? What does it look like for unvoiced ? | QMSum_120 |
false | Yeah. | QMSum_120 |
false | You have several some unvoiced ? | QMSum_120 |
false | The dif No. Unvoiced , I don't have | QMSum_120 |
false | Oh. | QMSum_120 |
false | for unvoiced. | QMSum_120 |
false | Yeah. So , you know , all | QMSum_120 |
false | I 'm sorry. | QMSum_120 |
false | But Yeah. | QMSum_120 |
false | Yeah. | QMSum_120 |
false | Yeah. This is the between | QMSum_120 |
false | This is another voiced example. Yeah. | QMSum_120 |
false | No. But it 's this , | QMSum_120 |
false | Oh , yeah. This is | QMSum_120 |
false | but between the frequency that we are considered for the excitation | QMSum_120 |
false | Right. Mm - hmm. | QMSum_120 |
false | for the difference and this is the difference. | QMSum_120 |
false | Yeah. | QMSum_120 |
false | This is the difference. OK. | QMSum_120 |
false | So , of course , it 's around zero , | QMSum_120 |
false | Yeah. | QMSum_120 |
false | Sure looks | QMSum_120 |
false | but | QMSum_120 |
false | Hmm. | QMSum_120 |
false | Well , no. | QMSum_120 |
false | Hmm. | QMSum_120 |
false | It is | QMSum_120 |
false | Yeah. Because we begin , uh , in fifteen point the fifteen point. | QMSum_120 |
false | So , does does the periodicity of this signal say something about the the | QMSum_120 |
false | Fifteen p | QMSum_120 |
false | So it 's Yeah. | QMSum_120 |
false | Pitch. | QMSum_120 |
false | It 's the pitch. | QMSum_120 |
false | the pitch ? | QMSum_120 |
false | Yeah. Mm - hmm. | QMSum_120 |
false | Yeah. | QMSum_120 |
false | OK. | QMSum_120 |
false | That 's like fundamental frequency. | QMSum_120 |
false | Mm - hmm. | QMSum_120 |
false | So , I mean , i t t | QMSum_120 |
false | OK. I see. | QMSum_120 |
false | I mean , to first order what you 'd what you 're doing I mean , ignore all the details and all the ways which is that these are complete lies. Uh , the the you know , what you 're doing in feature extraction for speech recognition is you have , uh , in your head a a a a simplified production model for speech , | QMSum_120 |
false | Mm - hmm. | QMSum_120 |
false | in which you have a periodic or aperiodic source that 's driving some filters. | QMSum_120 |
false | Mm - hmm. | QMSum_120 |
false | Yeah. This is the the auto - correlation the R - zero energy. | QMSum_120 |
false | Do you have the mean do you have the mean for the auto - correlation ? | QMSum_120 |
false | Uh , first order for speech recognition , you say " I don't care about the source ". | QMSum_120 |
false | For Yeah. | QMSum_120 |
false | Well , I mean for the the energy. | QMSum_120 |
false | I have the mean. | QMSum_120 |
false | Right ? | QMSum_120 |
false | Right. | QMSum_120 |
false | And so you just want to find out what the filters are. | QMSum_120 |
false | Right. | QMSum_120 |
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