A Comparison of DHMM and DTW for Isolated Digits Recognition System of Arabic Language

Abstract— Despite many years of concentrated research, the performance gap between automatic speech recognition (ASR) and human speech recognition (HSR) remains large. Especially for Arabic language, research efforts are still limited in comparison with other languages such as English or Japanese. In this work, we have use two algorithms to implement a system of Automatic Recognition of isolated Arabic Digits: Dynamic Time Warping (DTW) and Discrete Hidden Markov Model (DHMM). The endpoint detection, framing, normalization, Mel Frequency Cepstral Coefficient (MFCC) and vector quantization techniques were used to process speech samples to accomplish the recognition. The better recognition accuracy of about 92% was obtained with DHMM-based system. In noisy environment, the recognition performances for the two ASR are worse but the pattern recognition using HMM is better than the pattern using DTW.