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. The authors 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, the authors are able to obtain over 99% accuracy in the fast match for a continuous speech recognition task which has a vocabulary of 5000 words.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>