Obtaining candidate words by polling in a large vocabulary speech recognition system

Considers the problem of rapidly obtaining a short list of candidate words for more detailed inspection in a large vocabulary, vector-quantizing speech recognition system. An approach called polling is advocated, in which each label produced by the vector quantizer casts a varying, real-valed vote for each word in the vocabulary. The words receiving the highest votes are placed on a short list to be matched in detail at a later stage of processing. Expressions are derived for these votes under the assumption that for any given word, the observed label frequencies have Poisson distributions. Although the method is more general, particular attention is paid to the implementation of polling in speech recognition systems which use hidden Markov models during the acoustic match computation. Results are presented of experiments with speaker-dependent and speaker-independent Markov models on two different isolated word recognition tasks