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Low-Resource Speech Recognition of 500-Word Vocabularies

We describe techniques for enhancing the accuracy, efﬁciency and features of a low-resource, medium-vocabulary, grammar-based speech recognition system. Among the issues and techniques we explore are front-end speech / silence detection to reduce computational workload, the use of the Bayesian information 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 utterances. We report WER ﬁgures throughout, as appropriate.