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goatman · Guest

Hi there,
Just a quick question from a DSP beginner here. I'm doing a
college project on basic speech recognition, and I have some questions.
I am told to use a 256 point FFT with a hanning window and 50%
overlapping. This then produces a number of FFT windows right? (for
example, in a 512 sample sound file I will have 3 windows). Then I have
to perform an FFT on them. I then need to apply a Dynamic Time Warping
Algorithm (Sakoe&Chiba) to them. my problem is how do i turn my 3
feature vectors into 1? do i simply average them? or sum them? or have
i missed something very important. do i actually need a single feature
vector? what is a feature vector exactly? oh dear oh dear... I hope
that made sense to someone!
many thanks
joe
joewoodhouse30@hotmail.com

Ravi Srikantiah · Guest

Joe,

Are you planning on using the FFT components directly as the feature
vector? Typically, you'd smooth the spectrum in some manner rather than
using the spectrum coefficients directly.

You could smooth the spectrum essentially by applying a low-pass filter
to the spectrum coefficients, which is essentially multiplying the
"quefrency" domain coefficients with the low pass response. This is
called "liftering".

Also, usually this liftering is performed on the "cepstrum coefficients"
rather than on the time domain samples directly. If s represents your
speech, u the excitation, and h the filter coefficients of your source
model, then:

s = u * h * => convolution
F{s) = F(u}F{h}
log F{s) = log F{u} + log F{h}
Cepstrum{s} = Finv{log F{s}} = Finv{log F{u}} + Finv(log F{h}}

So by liftering the cepstrum you'll be shutting out the high frequency
excitation components and retaining the components that cause the slower
envelope variations that are more suitable for recognition. You might
also consider estimating LPC parameters and using a concatenated set of
LPC+Cepstrum coefficients as your vector for DTW.

Regards,
Ravi

goatman wrote:

Ravi Srikantiah · Guest

goatman · Guest

Thanks for the help ravi!
I am told to just use the amplitudes of frequencies bands as my feature
vectors. The cepstrum coeffients etc come in later. At the moment I
have got something working, but it gives extremely poor results (25/160
correct classifications...which is less thatn pure chance!) but from
what you said this could be expected

Andor · Guest

goatman wrote:

Ravi Srikantiah · Guest

Ravi Srikantiah · Guest

Don't forget to take care of the ends of your sample set. You should
have a half-window at either end for proper weighting - otherwise you'd
be incorrectly de-emphasising samples at both ends.

- Ravi

goatman · Guest

in the code assignment we are told to simply extend the last window by
the necessary number of zeros. so we have no half windows at all. will
that mean poor performance? at the moment i have the basic sakoe and
chiba up and running (i think) properly, and i get accuracy of about
28%. that seems rather low to me! although i suppose it is better than
pure chance

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