Continuous Mandarin speech recognition for Chinese language with large vocabulary based on segmental probability model

The author presents a study of large-vocabulary continuous Mandarin speech recognition based on a segmental probability model (SPM) approach. The SPM was found to be very suitable for recognition of isolated Mandarin syllables especially considering the monosyllabic structure of the Chinese language. To extend the application of the model to continuous Mandarin speech recognition, a concatenated syllable matching (CSM) algorithm in place of the conventional Viterbi search algorithm is first introduced. Also, to utilise the available training material efficiently, a training procedure is proposed to re-estimate the SPM parameters using the maximum a posteriori (MAP) algorithm. A few special techniques integrating acoustic and linguistic knowledge are developed further to improve the performance step by step. Preliminary experimental results show that the final achievable rate is as high as 91.62%, which indicates a 18.48% error rate reduction and more than three times faster than the well studied subsyllable-based CHMM