DocumentCode :
339172
Title :
A novel statistical language modelling method for continuous Chinese speech recognition
Author :
Bin, Tian ; Hongxin, Tian ; Qiang, Fu ; Kechu, Yi
Author_Institution :
Nat. Key Lab. on Integrated Service Networks, Xidian Univ., Xi´´an, China
fYear :
1998
fDate :
1998
Firstpage :
734
Abstract :
Statistical language models can play an important role in continuous speech recognition, but their performance is often unstable because of the training data sparsity. This paper proposes a statistical language modeling method, where the contribution of the language model is limited by the acoustic matching result and the N-gram probability distribution is modified referring to the length of the silence duration between adjacent syllables. Besides, the paper proposes a powerful single-state hidden Markov model (HMM) to model various kinds of silence segments
Keywords :
hidden Markov models; probability; speech recognition; statistical analysis; HMM; N-gram probability distribution; acoustic matching; adjacent syllables; continuous Chinese speech recognition; performance; silence duration; silence segments; single-state hidden Markov model; statistical language modelling; training data sparsity; Frequency estimation; Hidden Markov models; Intserv networks; Laboratories; Natural languages; Probability distribution; Smoothing methods; Speech recognition; Training data; Vocabulary;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Proceedings, 1998. ICSP '98. 1998 Fourth International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4325-5
Type :
conf
DOI :
10.1109/ICOSP.1998.770316
Filename :
770316
Link To Document :
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