Malay speech recognition in normal and noise condition

Abstract

Automatic speech recognition (ASR) is an area of research which deals with the recognition of speech by machine in several conditions. ASR performs well under restricted conditions (quiet environment), but performance degrades in noisy environments. This paper presents a simple experiment by using famous feature extraction method (LPC, LPCC and WLPCC) and simple kNN classifier to investigate the sensitivity of Malay speech digits to noise by adding 5dB white Gaussian noise. There are four steps to design and develop the Malay speech digits recognition system. They are Digit syllable structure and Malay speech corpus, end-point detection processing, feature extraction and classification method. The highest average recognition rates for Malays digits recognition is 96.22% that the feature vectors were derived from LPCC. The objective of this paper is to shown the occurrence of noise during Malay speech recognition.