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Test Of Vowels In Speech Recognition Using Continuous Density Hidden Markov Model And Development Of Phonetically Balanced-Words In The Filipino Language

Arnel C. Fajardo / Yoon-joong Kim
Published Online: 2014-08-15 | DOI: https://doi.org/10.2478/cplbu-2014-0092


An Automatic Speech Recognition (ASR) converts the speech signals into words. The recognized words can be the final output or it can be an input for a natural language processing. In this paper, vowel recognizer using Continuous density HMM and Mel-Frequency Cepstral Coefficient (MFCC) were used for feature extraction for its development, and phonetically balanced words (PBW) in Filipino were developed. Thus, this study is a preparation for Filipino Language ASR using HMM. For vowel recognizer, forty speakers were trained (20 male and 20 female speakers). An average accuracy rate of 94.5% was achieved for speaker-dependent test and 90.8% for speaker independent test. For PBW, 2 word lists were developed consisting of 257 words for the 2-syllable Filipino PBW word list and 212 words for the 3-syllable Filipino PBW word list.

Key words:: Continuous Density Hidden Markov Model; Filipino Vowels and Phonemes; Phonetically Balanced Words


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About the article

Published Online: 2014-08-15

Citation Information: Balkan Region Conference on Engineering and Business Education, Volume 1, Issue 1, Pages 531–536, ISSN (Online) 1843-6730, DOI: https://doi.org/10.2478/cplbu-2014-0092.

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© 2014 Quality Research Centre, Lucian Blaga University. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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