Isolated Malay Digit Recognition Using Pattern Recognition Fusion of Dynamic Time Warping and Hidden Markov Models
This paper is presents a pattern recognition fusion method for isolated Malay digit recognition using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). The aim of the project is to increase the accuracy percentage of Malay speech recognition. This study proposes an algorithm for pattern recognition fusion of the recognition models. The endpoint detection, framing, normalization, Mel Frequency Cepstral Coefficient (MFCC) and vector quantization techniques are used to process speech samples to accomplish the recognition. Pattern recognition fusion method is then used to combine the results of DTW and HMM which uses weight mean vectors. The algorithm is tested on speech samples that are a part of a Malay corpus. This paper has shown that the fusion technique can be used to fuse the pattern recognition outputs of DTW and HMM. Furthermore it also introduced refinement normalization by using weight mean vector to get better performance with accuracy of 94% on pattern recognition fusion HMM and DTW. Unlikely accuracy for DTW and HMM, which is 80.5% and 90.7% respectively.
Copyright: © 2008 S.A.R. Al-Haddad, S.A. Samad, A. Hussain and K.A. Ishak. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Endpoint detection
- Mel frequency cepstral coefficient
- vector quantization
- distance measurements and weight mean