Arabic digits speech recognition and speaker identification in noisy environment using a hybrid model of VQ and GMM

Abstract

This paper presents an automatic speaker identification and speech recognition for Arabic digits in noisy environment. In this work, the proposed system is able to identify the speaker after saving his voice in the database and adding noise. The mel frequency cepstral coefficients (MFCC) is the best approach used in building a program in the Matlab platform; also, the quantization is used for generating the codebooks. The Gaussian mixture modelling (GMM) algorithms are used to generate template, feature-matching purpose. In this paper, we have proposed a system based on MFCC-GMM and MFCC-VQ approaches on the one hand and by using the hybrid approach MFCC-VQ-GMM on the other hand for speaker modeling. The white Gaussian noise is added to the clean speech at several signal-to-noise ratio (SNR) levels to test the system in a noisy environment. The proposed system gives good results in recognition rate.

Biometric Technology Using Eigenface Algorithm

“…Selain pengenalan pola objek, kecerdasan buatan juga mampu melakukan identifikasi terhadap suara seperti pengenalan suara dengan jenis bahasa arab [6], kemudian identifikasi video secara realtime [7]. Selanjutnya mengklasifikasikan kandungan protein dari tepung terigu [8].…”