Frameworks for recognition of Mandarin syllables with tones using sub-syllabic units

Abstract

The recognition of Mandarin syllables is a key problem in large vocabulary Mandarin speech recognition. Conventionally, the tone and base syllable corresponding to a syllable are separately recognized by using a tone recognizer and a base syllable recognizer, respectively. In this paper, we propose a framework for Mandarin syllable recognition based on the classification of sub-syllabic units such as initials, finals and transitions. The final units are classified in accordance with the variations of tones to enhance the capability of tone discrimination. By using hidden Markov models (HMM) based on LPC-derived cepstral parameters, we develop a Mandarin syllable recognizer in which base syllables and their corresponding tones are jointly recognized. Experimental results indicate that the proposed syllable recognizer yields higher recognition rates than the conventional syllable recognizer does when sufficient amount of training data is used. We also show that the performance of the proposed syllable recognizer can be further improved with the incorporation of a tone recognizer.