Low-Resource Speech Recognition of 500-Word Vocabularies
We describe techniques for enhancing the accuracy, efficien cy and features of a low-resource, medium-vocabulary, gramma rbased speech recognition system. Among the issues and techniques we explore are front-end speech / silence detection t o reduce computational workload, the use of the Bayesian info rmation criterion ( BIC) to build smaller and better acoustic models, the minimization of finite state grammars, the use of hybrid maximum likelihood and discriminative models, and the automatic generation of baseforms from single new-word utt erances. We report WER figures throughout, as appropriate.