An improvement of initial-final based Mandarin continuous speech recognition

• Main Authors: S. M. Chiang, 蔣松茂
• Other Authors: S. H. Chen
• Format: Others
• Language: zh-TW
• Published: 1995
• Online Access: http://ndltd.ncl.edu.tw/handle/61597883163258818852

碩士 === 國立交通大學 === 電信研究所 === 83 === In this thesis, several techniques to improve the initial-final based HMM method for continuous Mandarin speech recognition are proposed. The baseline system uses 100 right-context-dependent initial HMM models and 39 context-independent final HMM models. First, the technique of bounded state duration is employed to model the temporal structure of speech signals and incorporated into the recognition process. The technique of syllable penalty is then used to relieve the suffering of high insertion errors. We then employ the technique of signal normalization to improve the system. The performance of the recognizer is then further improved by using gender-dependent HMM models. Effectiveness of the above proposals was confirmed by simulations on a speaker- independent speech recognition task to recognize continuous Mandarin speech through telephone channel. Syllable recognition rate was raised from 30.86% to 42.14%. Finally, an RNN-based finite state machine is proposed to pre-segment the input signal into 4 states including initial, final, silence, and transient states. State-dependent Constraints are then set to restrict the search of optimal path for relieving the computation load of the one-stage recognition process. Experimental results showed that about half of the computations can be saved with a very minor loss on the recognition rate.