A fast match for continuous speech recognition using allophonic models

Abstract

In a large vocabulary real-time speech recognition system, there is a need for a fast method for selecting a list of candidate words from the vocabulary that match well with a given acoustic input. In this paper we describe a highly accurate fast acoustic match for continuous speech recognition. The algorithm uses allophonic models and efficient search techniques to select a set of candidate words. The allophonic models are derived by constructing decision trees that query the context in which each phone occurs to arrive at an allophone in a given context. The models for all the words in the vocabulary are arranged in a tree structure and efficient tree search algorithms are used to select a. list of candidate words using these models. Using this method we are able to obtain over 99% accuracy in the fast, match for a continuous speech recognition task which has a vocabulary of 5,000 words.