Open vocabulary speech recognition with flat hybrid models

Abstract

Today’s speech recognition systems are able to recognize arbitrary sentences over a large but finite vocabulary. However, many important speech recognition tasks feature an open, constantly changing vocabulary. (E.g. broadcast news transcription, translation of political debates, etc. Ideally, a system designed for such open vocabulary tasks would be able to recognize arbitrary, even previously unseen words. To some extent this can be achieved by using sub-lexical language models. We demonstrate that, by using a simple flat hybrid model, we can significantly improve a well-optimized state-ofthe-art speech recognition system over a wide range of out-of-vocabulary rates. 1.