A hidden Markov model applied to Chinese four-tone recognition

In this paper, we present a probabilistic approach to Chinese four-tone recognition in which the well-known technique of a hidden Markov model is used. For each tone, a distinct hidden Markov model (HMM) is produced by using the Baum's forward-backward algorithm based upon the artificial (simulated) training sequences. Classification can be made by computing the probability of generating the test utterance with each tone model and choosing as the recognized tone the one corresponding to the model with the highest probability score. The recognition accuracies were found to be 98% for 35 Chinese phonetic alphabets pronounced by standard Chinese speakers and 96% for Chinese digits pronounced by our research group.