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

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

Caracterización analítica de segmentos del habla mediante clustering de vectores de coeficientes cepstrales

Este proyecto se enmarca dentro de una investigación mayor cuyo objetivo es establecer si distintos fragmentos de habla ocupan distintas regiones propias e independientes del espacio de vectores de características. En caso afirmativo, podrán sustituirse los modelos GMM de los reconocedores del habla convencionales por unidades discretas y deterministas que ayuden a reducir la dimensionalidad del problema afrontado por HMM. El primer paso de esa investigación constituye el contenido de este Proyecto Fin de Carrera, cuyo cometido es proponer un algoritmo de clustering que establezca la posibilidad de realizar una estudio analítico del habla a través de coeficientes cepstrales extraídos de fragmentos en castellano. Durante su desarrollo se realizarán pruebas que determinen la existencia de conjuntos de vectores de características, de manera que se pueda establecer una relación entre ellos y los bifonemas a los que corresponden sus vectores. Con este fin se utilizará el algoritmo de clasificación no supervisada Subtractive Clustering, que permitirá evitar la dificultad que supone el desconocimiento de la forma que esos conjuntos adoptan.This project belongs to a greater research which aims to identify a correspondence between speech fragments and regions from their feature vector space. If it were to be true, the GMM models used during the conventional speech recognition scheme could be replaced by discrete and deterministic units, helping to reduce the dimensionality of the problem solved by HMM. This Final Degree Project is the first step towards that goal. It will propose a clustering algorithm that uses the cepstral coefficients to characterize speech fragments. During its development, a series of experiments will be carried out by an unsupervised classification algorithm named Subtractive Clustering, which avoids the need of specifying some kind of model for those clusters there is no knowledge about.Ingeniería en Informátic