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Automatic speech recognition (ASR) for under-represented named-entity (UR-NE)is challenging due to such named-entities (NE) have insufficient instances andpoor contextual coverage in the training data to learn reliable estimates andrepresentations. In this paper, we propose approaches to enriching UR-NEs toimprove speech recognition performance. Specifically, our first priority is toensure those UR-NEs to appear in the word lattice if there is any. To this end,we make exemplar utterances for those UR-NEs according to their categories(e.g. location, person, organization, etc.), ending up with an improvedlanguage model (LM) that boosts the UR-NE occurrence in the word lattice. Withmore UR-NEs appearing in the lattice, we then boost the recognition performancethrough lattice rescoring methods. We first enrich the representations ofUR-NEs in a pre-trained recurrent neural network LM (RNNLM) by borrowing theembedding representations of the rich-represented NEs (RR-NEs), yielding thelattices that statistically favor the UR-NEs. Finally, we directly boost thelikelihood scores of the utterances containing UR-NEs and gain furtherperformance improvement.

Enriching Under-Represented Named-Entities To Improve Speech Recognition Performance