Speech recognition using a stochastic language model integrating local and global constraints

Ryosuke Isotani, Shoichi Matsunaga
1994 Proceedings of the workshop on Human Language Technology - HLT '94   unpublished
In this paper, we propose a new stochastic language model that integrates local and global constraints effectively and describe a speechrecognition system basedon it. Theproposedlanguagemodel uses the dependencies within adjacent words as local constraints in the same way as conventional word N-gram models. To capture the global constraints between non-contiguous words, we take into account the sequence of the function words and that of the content words which are expected to represent,
more » ... represent, respectively, the syntactic and semantic relationships between words. Furthermore, we show that assuming an independence between local-and global constraints, the number of parameters to be estimated and stored is greatly reduced. The proposed language model is incorporated into a speech recognizer based on the time-synchronous Viterbi decoding algorithm, and compared with the word bigram model and trigram model. The proposed model gives a better recognition rate than the bigram model, though slightly worse than the trigram model, with only twice as many parameters as the bigram model.
doi:10.3115/1075812.1075829 fatcat:l64tcw32lvdo7cxn3w7k6grlh4