Tone Recognition of Chinese Dissyllables Using Hidden Markov Models

Xinhui HU  Keikichi HIROSE  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E78-D   No.6   pp.685-691
Publication Date: 1995/06/25
Online ISSN: 
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Spoken Language Processing)
Category: 
Keyword: 
tone recognition,  chinese dissyllable,  hidden Markov modeling,  fundamental frequency,  tone contour,  macroscopic feature,  concatenated learning,  tone sandhi,  

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Summary: 
A method of tone recognition has been developed for dissyllabic speech of Standard Chinese based on discrete hidden Markov modeling. As for the feature parameters of recognition, combination of macroscopic and microscopic parameters of fundamental frequency contours was shown to give a better result as compared to the isolated use of each parameter. Speaker normalization was realized by introducing an offset to the fundamental frequency. In order to avoid recognition errors due to syllable segmentation, a scheme of concatenated learning was adopted for training hidden Markov models. Based on the observations of fundamental frequency contours of dissyllables, a scheme was introduced to the method, where a contour was represented with a series of three syllabic tone models, two for the first and the second syllables and one for the transition part around the syllabic boundary. Corresponding to the voiceless consonant of the second syllable, fundamental frequency contour of a dissyllable may include a part without fundamental frequencies. This part was linearly interpolated in the current method. To prove the validity of the proposed method, it was compared with other methods, such as representing all of the dissyllabic contours as the concatenation of two models, assigning a special code to the voiceless part, and so on. Tone sandhi was also taken into account by introducing two additional models for the half-third tone and for the first 4th tone of the combination of two 4th tones. With the proposed method, average recognition rate of 96% was achieved for 5 male and 5 female speakers.