Mohd Yusof, Shahrul Azmi and Mahat, Nor Idayu and Din, Roshidi and Yaakub, Abdul Razak and Yaacob, Sazali (2012) Noise robustness of Spectrum Delta (SpD) features in Malay vowel recognition. Computer Applications for Communication, Networking, and Digital Contents, 350. pp. 270-277. ISSN 1865-0929

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Abstract

In Malaysia, there is increasing number of speech recognition researchers focusing on developing independent speaker speech recognition systems that uses Malay Language which are noise robust and accurate.The performance of speech recognition application under adverse noisy condition often becomes the topic of interest among speech recognition researchers regardless of the languages in use. This paper present a study of noise robust capability of an improved vowel feature extraction method called Spectrum Delta (SpD).The features are extracted from both original data and noise-added data and classified using three classifiers; (i) Multinomial Logistic Regression (MLR), (ii) K-Nearest Neighbors (k-NN) and (iii) Linear Discriminant Analysis (LDA).Results show that the proposed SpD is robust towards noise and LDA performs the best in overall vowel classification compared to MLR and k-NN in terms of robustness capability especially with signal-to-noise (SNR) above 20dB.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Dr. Shahrul Azmi Mohd. Yusof
Date Deposited:	15 Apr 2014 08:04
Last Modified:	15 Apr 2014 08:04
URI:	https://repo.uum.edu.my/id/eprint/10141

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