NDLI: Malay speech recognition in normal and noise condition

Content Provider	IEEE Xplore Digital Library
Author	Fook, C.Y. Hariharan, M. Yaacob, S. Ah, A.
Copyright Year	2012
Description	Author affiliation: School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Ulu Pauh, 02600, Arau, Perlis, Malaysia (Fook, C.Y.; Hariharan, M.; Yaacob, S.; Ah, A.)
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.
Starting Page	409
Ending Page	412
File Size	810796
Page Count	4
File Format	PDF
ISBN	9781467309608
e-ISBN	9781467309615
DOI	10.1109/CSPA.2012.6194759
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-03-23
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	LPCC Malays Speech Recognition k-NN classifier Gaussian noise Hidden Markov models WLPCC Speech recognition white Gaussian noise Speech Feature extraction features extraction end-point detection Speech processing LPC
Content Type	Text
Resource Type	Article

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