Decision Fusion for Isolated Malay Digit Recognition Using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM)

This paper is focused on Malay speech recognition with the intention to introduce a decision fusion technique for isolated Malay digit recognition using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM). This study proposes an algorithm for decision 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. Decision fusion technique is then used to combine the results of DTW and HMM. The algorithm is tested on speech samples that is a part of a Malay corpus.