Discriminative pronunciation learning for speech recognition for resource scarce languages

  • Authors:
  • Hao Yee Chan;Roni Rosenfeld

  • Affiliations:
  • Carnegie Mellon University, Pittsburgh;Carnegie Mellon University, Pittsburgh

  • Venue:
  • Proceedings of the 2nd ACM Symposium on Computing for Development
  • Year:
  • 2012

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Abstract

In this paper, we describe a method to create speech recognition capability for small vocabularies in resource-scarce languages. By resource-scarce languages, we mean languages that have a small or economically disadvantaged user base which are typically ignored by the commercial world. We use a high-quality well-trained speech recognizer as our baseline to remove the dependence on large audio data for an accurate acoustic model. Using cross-language phoneme mapping, the baseline recognizer effectively recognizes words in our target language. We automate the generation of pronunciations and generate a set of initial pronunciations for each word in the vocabulary. Next, we remove potential conflicts in word recognition by discriminative training.