Acoustic unit discovery and pronunciation generation from a grapheme-based lexicon

We present a framework for discovering acoustic units and generating an associated pronunciation lexicon from an initial grapheme-based recognition system. Our approach consists of two distinct contributions. First, context-dependent grapheme models are clustered using a spectral clustering approach to create a set of phone-like acoustic units. Next, we transform the pronunciation lexicon using a statistical machine translation-based approach. Pronunciation hypotheses generated from a decoding of the training set are used to create a phrase-based translation table. We propose a novel method for scoring the phrase-based rules that significantly improves the output of the transformation process. Results on an English language dataset demonstrate the combined methods provide a 13% relative reduction in word error rate compared to a baseline grapheme-based system. Our approach could potentially be applied to low-resource languages without existing lexicons, such as in the Babel project.