The first successfully implemented real-time Mandarin dictation machine, which recognizes Mandarin speech with very large vocabulary and almost unlimited texts for the input of Chinese characters into computers, is described. The machine is speaker-dependent, and the input speech is in the form of sequences of isolated syllables. The machine can be decomposed into two subsystems. The first subsystem recognizes the syllables using hidden Markov models. Because every syllable can represent many different homonym characters and form different multisyllabic words with syllables on its right or left, the second subsystem is needed to identify the exact characters from the syllables and correct the errors in syllable recognition. The real-time implementation is on an IBM PC/AT, connected to three sets of specially designed hardware boards on which seven TMS 320C25 chips operate in parallel. The preliminary test results indicate that it takes only about 0.45 s to dictate a syllable (or character) with an accuracy on the order of 90%.>

The recognition of a total of 408 very confusing Mandarin syllables is very difficult because this vocabulary consists of 38 confusing sets, each of which can have as many as 19 syllables. The recognition of these 408 syllables becomes even more difficult when only very limited training data are available. A special direct-concatenation approach for training hidden Markov models (HMMs) to recognize these syllables with very limited training data is developed in which each syllable is divided into INITIAL and FINAL parts and 408 right-context-dependent INITIAL HMMs and 38 left-context-independent FINAL HMMs are separately trained and the transition region carefully taken account of, and then these INITIAL and FINAL HMMs are directly concatenated to form syllable recognition. Experimental results show that this approach can utilize the very limited training data most efficiently and provide significant improvements in recognition performance. Although the results are obtained for Mandarin syllables, the approach is believed to be equally helpful for the recognition of other confusing vocabularies.>

A successfully implemented real-time Mandarin dictation machine which recognizes Mandarin speech with unlimited texts and very large vocabulary for the input of Chinese characters to computers is described. Isolated syllables including the tones are first recognized using specially trained hidden Markov models with special feature parameters. The exact characters are then identified from the syllables using a Markov Chinese language model. The real-time implementation is on an IBM PC/AT, connected to a set of special hardware boards on which ten TMS 320C25 chips operate in parallel. It takes only 0.45 s to dictate a character.>

Two types of hidden Markov modeling (HMM), discrete-type (vector quantization (VQ)-based) and mixture density, are used to overcome part of the problem of Mandarin syllable recognition. In the VQ-based HMM part, a two-stage process is used to generate a VQ codebook in the hope of avoiding the possible domination of the vowel signal vectors on the codewords in vector space. A polyobservation sequence approach is adopted to minimize the quantization error. In the mixture density approach, the context-dependent models are incorporated in the classification of different buffers in the memory. The 408 syllables are divided into 154 buffers, 115 for consonants and 39 for vowels. In this way a buffer with tied probabilities may be shared by several different syllables. Hence, the bias between the confusing syllables with the same vowel part is expected to be eliminated by tied probability techniques. Experimental results show that improvement can be achieved with both types of HMM.>