Elektronika 2009-11.pdf - Instytut Systemów Elektronicznych

definite temporary partition. Use of this two methods permits
onto proper emission of signal audio, and correct realizing remaining
stages of system. Suitable connection of both methods
permits onto correct delimitation of beginning and end of
signal. After delimitation of beginning and end of polish word, to
further operation signal is design without superfluous silence.
The non-stationary nature of the speech signal caused by
dynamic proprieties of human speech result in dependence
of the next stage on use of division of entrance signal onto
stationary frame boxes [5]. Signal is stationary in short temporary
partitions (10 ± 30 ms) [7]. Every such stationary frame
box was replaced by symbol of observation in process of create
of vectors of observation. In created system it was assumed
that length of every frame box equals 30 ms, what at
given sampling of signal (8 kHz) remove 240 samples. For
speech recognition, in aim of keeping stationary of signal, it
was assumed that every next frame box is sew on previous
with delay. It was accepted, that 80 last samples of signal of
previous frame box are simultaneously 80 samples of next
frame box. For speaker verification, in aim to keep all the detail
signal, all frame boxes do not overlap.
The mechanism of cepstral speech analysis
Speech processing applications require specific representations
of speech information. A wide range of possibilities exists
for parametrically representing the speech signal. Among
these the most important parametric representation of speech
is short time spectral envelope [5,7]. Linear Predictive Coding
(LPC) and Mel Frequency Cepstral Coefficients (MFCC)
spectral analysis models have been used widely for speech
recognition applications. Usually together with MFCC coefficients,
first and second order derivatives are also used to take
into account the dynamic evolution of the speech signal, which
carries relevant information for speech recognition.
In the mel-cepstrum, the spectrum were first passed
through mel-frequency-bandpass-filters before they were
transformed to the frequency domain [8]:
The characteristics of filters followed the characteristics old
human auditory system [8]. The filters had triangular bandpass
frequency responses. For speech recognition, it was
used filters with width 300 mels, and transferred with a delay
150 mels. For speaker verification, it was used filters with
width 200 mels, and transferred with a delay 100 mels, for
a more detailed analysis of low frequency. The bands of filters
were spaced linearly for bandwidth below 1000 Hz and increased
logarithmically after the 1000 Hz. In the mel-frequency
scaling, all the filter bands had the same width, which
were equal to the intended characteristic of the filters, when
they were in normal frequency scaling. Spectrum of signal of
every frame boxes obtained by Fast Fourier Transform
(FFT,512) comes under process of filtration by bank of filters.
The next step was to calculate the members of each filter by
multiplying the filter’s amplitude with the average power spectrum
of the corresponding frequency of the voice input. The
summation of all members of a filters is:
(1)
(2)
Finally, the mel-frequency cepstrum coefficients (MFCC) was
derived by taking the log of the mel-power-spectrum coefficients
S k then convert them back to time (frequency) domain
using Discrete Cosine Transform (DCT). The number of melcoefficients
K used, for speaker recognition purposes, was
usually from 12 to 20 [8]:
In practice, removing this one from the formula gave
a better performance, both for speech recognition, and verification
of user.
In this work, for speech cooding, it was used twenty dimensional
MFCC as the standard audio features. For speech
recognition, aim of analysis of signal audio was coding of signal
audio, and obtaining of entrance data in form of vectors of
observation. Polish language contains 37 different phonems,
therefore to coding of signal audio it was applied codebook
including 37 code symbols. At frequency of sampling 8 kHz,
instead of 8000 values, signal audio will be coded by about
50 values. It was applied Lloyd algorithm to vector quantization.
One from basic operation during vector quantization is
delimitation of distance of next vector of observation from all
center of gravity of codebook. To measurement of distance it
was applied Euclidean measure. For speaker verification, obtained
for all frame boxes cepstrum coefficients add upped
properly. In this expedient all independent statement one
coded by twenty cepstral coefficients.
Vector quantization with use Lloyd
algorithm
In use of loss-free compression, generated data by source
have to be represented by one from small number of code
words. Number of possible different data is generally larger
from number of code word, design to them of representing.
Process of representing of large collection of value by collection
considerably smaller is called quantization [9]. A vector
quantizer Q of dimension M and size N is a mapping from
a vector x in M-dimensional Euclidean space R M into a finite
set Y containing NM-dimensional outputs or reproduction
points, called code vectors or code words. Thus:
where:
Y is known as the codebook of the quantizer. The mapping
action is written as:
(3)
(4)
(5)
(6)
66 ELEKTRONIKA 11/2009