SPEAKER IDENTIFICATION USING MEL FREQUENCY CEPSTRAL COEFFICIENTS
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1. INTRODUCTION
Speech is one of the natural forms of
communication. Recent development has made it
possible to use this in the security system. In
speaker identification, the task is to use a speech
sample to select the identity of the person that
produced the speech from among a population of
speakers. In speaker verification, the task is to use a
speech sample to test whether a person who claims
to have produced the speech has in fact done so[1].
This technique makes it possible to use the speakers’
voice to verify their identity and control access to
services such as voice dialing, banking by telephone,
telephone shopping, database access services,
information services, voice mail, security control for
confidential information areas, and remote access to
computers.
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2. PRINCIPLES OF SPEAKER
RECOGNITION
Speaker recognition methods can be divided into
text-independent and text-dependent methods. In a
text-independent system, speaker models capture
characteristics of somebody’s speech which show up
irrespective of what one is saying. [1]
In a text-dependent system, on the other hand, the
recognition of the speaker’s identity is based on his
or her speaking one or more specific phrases, like
passwords, card numbers, PIN codes, etc.


The MFCC processor
A block diagram of the structure of an MFCC
processor is given in Figure 1. The speech input is
recorded at a sampling rate of 22050Hz. This
sampling frequency is chosen to minimize the
effects of aliasing in the analog-to-digital
conversion process.


Mel-frequency wrapping
The speech signal consists of tones with different
frequencies. For each tone with an actual
Frequency, f, measured in Hz, a subjective pitch is
measured on the ‘Mel’ scale. The mel-frequency
scale is a linear frequency spacing below 1000Hz
and a logarithmic spacing above 1000Hz. As a
reference point, the pitch of a 1kHz tone, 40dB
above the perceptual hearing threshold, is defined as
1000 mels. Therefore we can use the following
formula to compute the mels for a given frequency f


4. FEATURE MATCHING
The state-of-the-art feature matching techniques
used in speaker recognition include, Dynamic Time
Warping (DTW), Hidden Markov Modeling
(HMM), and Vector Quantization (VQ). The VQ
approach has been used here for its ease of
implementation and high accuracy.


LBG design algorithm
The LBG VQ design algorithm is an iterative
algorithm (as proposed by Y. Linde, A. Buzo & R.
Gray) which alternatively solves optimality criteria
[7]. The algorithm requires an initial codebook. The
initial codebook is obtained by the splitting method.
In this method, an initial codevector is set as the
average of the entire training sequence. This
codevector is then split into two.