http://lattes.cnpq.br/0405270628677774; MELO, Fabrício Gutemberg Lélis de.
Abstract:
Biometric identification of individuals has been widely used as a security
mechanism for accessing computer systems or restricted environments. Biometric systems have been developed to perform identification through fingerprint, iris, or
voice, for example. Using the voice as a biometric identifier has been increasingly
possible due to significant advances in digital processing of speech signals area.
This research aims to evaluate the efficiency of mel-frequency cepstral coefficients
in the representation of the characteristics of a speaker in an automatic speaker
verification. The techniques used to construct the automatic speaker verification
system aiming at a hardware implementation included the use of: (i) melfrequency
cepstral coefficients, like feature vector; (ii) vector quantization, in
patterning modelling; and (iii) a decision rule, based on Euclidean distance. The
system used for evaluation in the representation of the characteristics of a speaker
is a modification of another automatic speaker verification system using linear
predictive coding coefficients for the representation of the vocal characteristics of
a speaker. It was implemented using C++ for the training phase, and
SystemVerilog for the verification phase. The results using mel-frequency cepstral
coefficients were 99.34% in the hit rate, 0.17% to error rate and 0.49% to
unknown response rate, compared respectively to 96.52% in success rate, 0.90%
to error rate and 2.58% to unknown rate using the linear predictive coding
coefficients.