| Summary: | Humans interact with one another in several ways such as speech, body
language, and so on. Among them, speech communication is the most common in
human-to-human interaction. Speech signal is a rich source of information and
convey more than spoken words. The additional information conveyed in speech
includes gender information, age, speaker�s identity and health. This research
studies text-dependent speaker identification based on acoustic feature using
Multiclass Support Vector Machines (SVMs) with one-versus-one (OVO)
approach on a review of feature vector. Feature vectors, are adopted as a feature
used, were obtained from three methods, namely Linear Predictive Cepstral
Coefficient (LPCC), Mel-Frequency Cepstral Coefficient (MFCC), and a
combination of both. Parameters of this system are variations of the number of
features, the penalty factor, and the kernel function. The best result achieved is
93.75% identification rate for 26 number of feature combination. Futhermore, the
accuracy of identification with the increase of penalty factor, and Gaussian RBF
kernel function results better than Polynomial kernel. Gaussian RBF kernel
function achieves 85.11% identification rate, while in Polynomial kernel achieves
84.43 %.
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