A nonparametric Bayesian approach for automatic discovery of a lexicon and acoustic units

State of the art speech recognition systems use context-dependent phonemes as acoustic units. However, these approaches do not work well for low resourced languages where large amounts of training data or resources such as a lexicon are not available. For such languages, automatic discovery of acoustic units can be important. In this paper, we demonstrate the application of nonparametric Bayesian models to acoustic unit discovery. We show that the discovered units are linguistically meaningful. We also present a semi-supervised learning algorithm that uses a nonparametric Bayesian model to learn a mapping between words and acoustic units. We demonstrate that a speech recognition system using these discovered resources can approach the performance of a speech recognizer trained using resources developed by experts. We show that unsupervised discovery of acoustic units combined with semi-supervised discovery of the lexicon achieved performance (9.8% WER) comparable to other published high complexity systems. This nonparametric approach enables the rapid development of speech recognition systems in low resourced languages.