Comparison of HMM and DTW for isolated word recognition system of Punjabi language

Abstract

Issue of speech interface to computer has been capturing the global attention because of convenience put forth by it. Although speech recognition is not a new phenomenon in existing developments of user-machine interface studies but the highlighted facts only provide promising solutions for widely accepted language English. This paper presents development of an experimental, speaker-dependent, real-time, isolated word recognizer for Indian regional language Punjabi. Research is further extended to comparison of speech recognition system for small vocabulary of speaker dependent isolated spoken words in Indian regional language (Punjabi) using the Hidden Markov Model (HMM) and Dynamic Time Warp (DTW) technique. Punjabi language gives immense changes between consecutive phonemes. Thus, end point detection becomes highly difficult. The presented work emphasizes on template-based recognizer approach using linear predictive coding with dynamic programming computation and vector quantization with Hidden Markov Model based recognizers in isolated word recognition tasks, which also significantly reduces the computational costs. The parametric variation gives enhancement in the feature vector for recognition of 500-isolated word vocabulary on Punjabi language, as the Hidden Marko Model and Dynamic Time Warp technique gives 91.3% and 94.0% accuracy respectively. © 2010 Springer-Verlag.