[PD] speaker recognition with pd ?

[Hans-Christoph Steiner]

On Sep 22, 2011, at 4:13 PM, Charles Henry wrote:

> On Thu, Sep 22, 2011 at 12:42 PM,  <gnd at itchybit.org> wrote:
>
>> The task would be to identify from a live-talk the voice of the  
>> current
>> speaker amongst several. Training before is also possible .. i  
>> guess this
>> could be done for sure by utilizing a simple neural network trained  
>> on a
>> FFT docemposition of the voices..  so there must be some software  
>> out for
>> sure...
>
> Something tells me a fft+neural network would be really bad at this.
> Seriously, that sounds like a doomed project if you tried.  These
> things would be huge:
> 1.  fft size (for resolution)
> 2.  network size (based on the fft size)
> 3.  training set (lots of variance in the speaker is possible)
>
> How about autocovariance and dot-product?
>
> Ahead of time, create an array containing normalized autocovariance
> (an autocorrelation) of the speaker's voice.
>
> Compute a running autocovariance of the sound.  Decompose it into the
> portion of the sound matching the autocovariance of the speaker and
> compare it with the part not matching the speaker (via dot-product, or
> projection operators)
>
> That would be ~less~ expensive and time consuming than neural
> networks, but I'd give it not much chance of success either.  Probably
> it would match quite a few different people all the same.


I think that getting some kind of basic recognition of who is speaking  
would not be super difficult, if you have a clean recording of the  
voices. You need to get the formant of the voice, then use that as the  
base comparison.  You could start with something like William Brent's  
timbreID library to isolate the different vowel sounds, then get a  
format for each of the vowels, then use that data for the pattern  
matching.  It'll definitely take some research and a solid chunk of  
work to get it going.

.hc

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