Using tone information in Cantonese continuous speech recognition

Abstract

In Chinese languages, tones carry important information at various linguistic levels. This research is based on the belief that tone information, if acquired accurately and utilized effectively, contributes to the automatic speech recognition of Chinese. In particular, we focus on the Cantonese dialect, which is spoken by tens of millions of people in Southern China and Hong Kong. Cantonese is well known for its complicated tone system, which makes automatic tone recognition very difficult. This article describes an effective approach to explicit tone recognition of Cantonese in continuously spoken utterances. Tone feature vectors are derived, on a short-time basis, to characterize the syllable-wide patterns of F0 (fundamental frequency) and energy movements. A moving-window normalization technique is proposed to reduce the tone-irrelevant fluctuation of F0 and energy features. Hidden Markov models are employed for context-dependent acoustic modeling of different tones. A tone recognition accuracy of 66.4% has been achieved in the speaker-independent case. The recognized tone patterns are then utilized to assist Cantonese large-vocabulary continuous speech recognition (LVCSR) via a lattice expansion approach. Experimental results show that reliable tone information helps to improve the overall performance of LVCSR.