Context-dependent acoustic models for Chinese speech recognition

Selecting good speech units and building precise acoustic models on these units are basic problems for HMM speech recognition systems. In this paper, we review some distinguished phonetic features of the Chinese language and show how these features could be considered for getting a better solution of speech units selection and acoustic model building. The initials and finals are suggested to be the recognition units. Several experiments have been carried out to find those proper acoustic models which can represent the co-articulation among initials and finals more accurately and give a higher recognition rate. Results of recognition error rate under different acoustic models are given and some comparisons are made. Experiments show that not only the intra-syllable context-dependent acoustic models but also the inter-syllable acoustic models are necessary for reducing recognition error rate of words and continuous speech recognition. Experiments also show the tone-pattern information is important for accurate syllable recognition