Abstract

This paper presents a technique for modeling pronunciation in automatic speech recognition using an approach based on statistical machine translation. The task of a pronunciation model in speech recognition is to convert a sequence of phonemes into proper words of the language. This task can be realized as a machine translation approach, whereby the source language is a sequence of phonemes and the target language is a sequence of letters forming words. The model presented in this paper specially targets out-of-vocabulary words or words with several different pronunciations. A dynamic string alignment algorithm is applied to learn the phoneme-to-letter alignment. Pronunciation segments are then extracted from these alignments and are used by the decoder to recognize words from an unknown sequence of phonemes. In contrast to usual statistical machine translation decoding, the decoder presented here uses a probabilistic finite state model rather than normal n-gram language models. A number of experiments were performed using the CMU pronunciation dictionary and the results obtained are quite promising, even where only small amounts of training data are used. Index Terms: speech recognition, pronunciation modeling 1.

Citations