IDENTIFIKASI PENUTUR BERBASIS POLA AKUSTIK MENGGUNAKAN SUPPORT VECTOR MACHINE - , Ari Fadli, Dr. Eng Ir. Risanuri Hidayat, M.Sc. ([Yogyakarta] : Universitas Gadjah Mada, 2012)
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, speakerâs 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 %.