Abstract
Dynamic Time Warping (DTW) is template based cost minimization technique. We propose Hidden Markov Model (HMM) based enhanced DTW technique to efficiently recognize various speaking rate signals and for recognizing closely similar utterances. We extend the derivation of Viterbi and forward algorithms for finding optimized path alignment in new propose technique and extend the Baum-Welch algorithm to optimize the model parameters. The proposed technique is compared with conventional DTW technique, and from comparative results analysis we find that it improves the results from 84% to 94 % using DTW technique for Hindi spoken words for various speech utterances in different environmental conditions or for varying speakers.
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Kumar, S.K., Kant, L.K., Shachi, S. (2013). HMM Based Enhanced Dynamic Time Warping Model for Efficient Hindi Language Speech Recognition System. In: Das, V.V., Chaba, Y. (eds) Mobile Communication and Power Engineering. AIM 2012. Communications in Computer and Information Science, vol 296. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35864-7_28
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DOI: https://doi.org/10.1007/978-3-642-35864-7_28
Publisher Name: Springer, Berlin, Heidelberg
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