Mandarin telephone speech recognition for automatic telephone number directory service

This paper discusses an HMM-based Mandarin telephone speech recognition method for implementing a prototype system of automatic telephone number directory service. It adopted the GPD/MCE training algorithm to train the HMM models for 100 final-dependent syllable initials and 40 syllable finals. The SBR method was used to compensate the speaker and channel effects. Besides, a recurrent neural network (RNN) based pre-classification scheme was employed to speed up the recognition search. A syllable recognition rate of 53.7% was achieved. This method was then used to implement an isolated-word recognizer for the prototype system to discriminate 1922 names of bank and insurance companies. Word recognition rates of 94.8% for top-1 and 97.9% for top-3 were achieved