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Segmental probability distribution model approach for isolated Mandarin syllable recognition

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1 Author(s)
Shen, J.-L. ; Inst. of Inf. Sci., Acad. Sinica, Taipei, Taiwan

A segmental probability distribution model (SPDM) approach is proposed for fast and accurate recognition of isolated Mandarin syllables. Instead of the conventional frame-based approach such as the hidden Markov model (HMM), the model matching process in the proposed SPDM is evaluated segment-by-segment based on information-theoretic distance measurements. The training and recognition procedures for the SPDM are developed first. Several distance measurement criteria, including the Chernoff distance, Bhattacharyya distance, Patrick-Fisher (1969) distance, divergence and a Bayesian-like distance, are used, and formulations and comparative results are discussed. Experimental results show that, compared to the widely used sub-unit based continuous density HMM, the proposed method leads to an improvement of 15.27% in the error rate, with a 12-fold increase in recognition speed and less than three quarters of the mixture requirements

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Vision, Image and Signal Processing, IEE Proceedings -  (Volume:145 ,  Issue: 6 )