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Feature Parameter Extraction and Speech Recognition Using Matrix Factorization  

Lee Kwang-Seok (진주산업대학교 전자공학과)
Hur Kang-In (동아대학교 전자공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.10, no.7, 2006 , pp. 1307-1311 More about this Journal
Abstract
In this paper, we propose new speech feature parameter using the Matrix Factorization for appearance part-based features of speech spectrum. The proposed parameter represents effective dimensional reduced data from multi-dimensional feature data through matrix factorization procedure under all of the matrix elements are the non-negative constraint. Reduced feature data presents p art-based features of input data. We verify about usefulness of NMF(Non-Negative Matrix Factorization) algorithm for speech feature extraction applying feature parameter that is got using NMF in Mel-scaled filter bank output. According to recognition experiment results, we confirm that proposed feature parameter is superior to MFCC(Mel-Frequency Cepstral Coefficient) in recognition performance that is used generally.
Keywords
Speech Feature Extraction; Non-Negative Matrix Factorization; Mel-scaled Filter Bank;
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