Large Vocabulary Natural Language Continuous Speech Recognition

Abstract

The present paper describes our current research on automatic speech recognition of continuously read sentences from a naturally-occurring corpus: office correspondence. The recognition system combines features from our current isolated-word recognition system and from our previously developed continuous speech recognition systems. It consists of an acoustic processor, an acoustic channel model, a language model, and a linguistic decoder. Some new features in the recognizer relative to our isolated-word speech recognition system include the use of a fast match to rapidly prune to a manageable number the candidates considered by the detailed match, multiple pronunciations of all function words, and modelling of interphone coarticulatory behavior. To date, we have recorded training and test data from a set of 10 male talkers. The test data consist of 50 sentences drawn from spontaneously generated memos covered by a 5000 word vocabulary. The perplexity of the test sentences was found to be 93; none of the sentences were part of the data used to generate the language model. Preliminary (speaker-dependent) recognition results on these talkers yielded an average word error rate of 11.0%.