Continuous-speech recognition using a stochastic language model

The authors describe the design of a stochastic language model and its integration into a continuous-speech recognition system that is part of the SPICOS system for understanding database queries spoken in natural language. The recognition strategy is based on statistical decision theory. The stochastic language model for the recognition of database queries is based on probabilities of trigrams, bigrams, and unigrams of word categories, which are intended to reflect lexical and semantic aspects of the SPICOS task. The implementation of stochastic language models in the search procedure is described, and results of recognition experiments are given. By using a stochastic model (perplexity = 124) a reduction of the word error rate from 21.8% without language model (perplexity = 917) to 9.1% was achieved