Robust Speech Recognition System Using Conventional and Hybrid Features of MFCC,LPCC,PLP,RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions

作     者：Veton Z.Kepuska Hussien A.Elharati 

作者机构：Electrical&Computer Engineering DepartmentFlorida Institute of TechnologyMelbourneFLUSA 

出 版 物：《Journal of Computer and Communications》 (电脑和通信（英文）)

年 卷 期：2015年第3卷第6期

页      面：1-9页

学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统] 

主　　题：Speech Recognition Noisy Conditions Feature Extraction Mel-Frequency Cepstral Coefficients Linear Predictive Coding Coefficients Perceptual Linear Production RASTA-PLP Isolated Speech Hidden Markov Model 

摘      要：In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.