Stochastic language models for speech recognition and understanding
Giuseppe Riccardi, Allen L. Gorin · 1998
Stochastic language models for speech recognition have traditionally been designed and evaluated in or-der to optimize word accuracy. In this work, we present a novel framework for training stochastic language models by optimizing two different criteria appropri-ate for speech recognition and language understand-ing. First, the language entropy and salience measure are used for learning the relevant spoken language features (phrases). Secondly, a novel algorithm for training stochastic finite state machines is presented which incorporates the acquired phrase structure into a single stochastic language model. Thirdly, we show the benefit of our novel framework with an end-to-end evaluation of a large vocabulary spoken language system for call routing. 2.