A comparative study on the performance of several speech recognition techniques applied on the highly confusing mandarin syllables

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Abstract
In this paper, the performance of several speech recognition techniques applied on the highly confusing Mandarin syllables were carefully compared, including dynamic time warping (DTW), the newly proposed DTW with superimposed weighting function (DTWW), the discrete hidden Markov models (DHMM) and the continuous hidden Markov models (CHMM). The vocabulary used here consists of 409 first tone isolated Mandarin syllables. Due to the fact that many confusing sets exist in this vocabulary, the accurate recognition of these syllables is relatively difficult, and all the recognition experiments were performed in the speaker dependent mode. After a series of 13 experiments, it was found that the recognition rate of the newly proposed DTWW (88.3) is higher than that of DTW (85.1), DHMM (65.0) and CHMM (83.9), and that the CPU time used for DTWW is 1.03 times that for DTW, 24 times that for DHMM and 4.3 times that for CHMM. In addition, the memory space required for DTWW and DTW is 3.4 times that of DHMM and 8.5 times that of CHMM. Therefore, DTWW has the highest recognition rate, DHMM has the fastest recognition speed, whereas CHMM appears to be very attractive when all the different factors including recognition rate, recognition speed and memory space requirement are considered.

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