i am given a project on speech features extraction. so would you please send me the codes to nextbornson[at]gmail.com

feature extraction from speech using mfcc matlab coding

Abstract

Speech recognition has wide range of applications in security systems, healthcare, telephony military, and equipment designed for handicapped. Speech is continuous varying signal. So, proper digital processing algorithm has to be selected for automatic speech recognition system. To obtain required information from the speech sample, features have to be extracted from it. For recognition purpose the feature are analyzed to make decisions. In this paper implementation of Speech recognition system in MATLAB environment is explained. Mel-Frequency Cepstral Coefficients (MFCC) and Dynamic Time Wrapping (DTW) are two algorithms adapted for feature extraction and pattern matching respectively. Results are obtained by one time training and continuous testing phases.This paper introduces the basic theory of speech recognition, including speech signal pre-emphasis, endpoint detection and feature extraction, pattern recognition. On this basis, we introduce the MFCC (Mel-frequency cepstral coefficients) specific extraction method. Secondly, from the recognition rate and improve the speed of approach to identify proposed DTW (dynamic time warping) algorithm to improve the research, in order to achieve a more satisfactory effect of speech recognition. Experimental results show that the sets of speech recognition system to identify the model to meet the general conditions of the application.

Introduction

This paper presents the performance of feature extraction techniques for speech recognition, for the classification of speech represented by a particular continuous sentence model. The goal of this study is to present independent as well as comparative performances of popular appearance based feature extraction techniques i.e. Linear Discriminative Analysed and Mel Frequency Cestrum Coefficient. Mel Frequency Cepstrum Coefficient (MFCC) helps us in extracting feature where as linear discriminant analysis (LDA) is used for reducing dimension of extracted feature. We experimented MFCC feature extraction individually and proposed a Fusion of MCCC and LDA for feature extraction.