Research on Acoustic Model of Large-vocabulary Continuous Speech Recognition for Lhasa Tibetan
Meng Meng · Jisuanji gongcheng · 2012
The characteristics of Tibetan are analyzed in this paper.The framework of auto speech recognition of Lhasa dialect is designed.Several feasible units for acoustic models are analyzed.Contextual continuous Hidden Markov Model(HMM) models based on phonemes and semi-syllables are established and trained on Hidden Markov Model Toolkit(HTK) platform respectively and large-vocabulary continuous speech recognition of Lhasa Tibetan is implemented.Experimental results show that Word Error Rate(WER) is 7.8% in the best case.