Improving Non-native Speech Recognition Performance by Discriminative Training for Language Model in a CALL System

@inproceedings{Wang2011ImprovingNS,
  title={Improving Non-native Speech Recognition Performance by Discriminative Training for Language Model in a CALL System},
  author={Hongcui Wang and Tatsuya Kawahara and Yuguang Wang},
  year={2011}
}
High non-native speech recognition performance is always a challenge for a CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning. Conventionally, possible error patterns, based on linguistic knowledge, are added to the ASR grammar network. However, the effectiveness of this approach depends much on the prior linguistic knowledge. In this paper, we design a new scheme for error prediction using two sequential machine learning… CONTINUE READING

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