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Improving nonnative speech understanding using context and N-best meaning fusion

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2 Author(s)
Yushi Xu ; Comput. Sci. & Artificial Intell. Lab., MIT, Cambridge, MA, USA ; Seneff, S.

Speech understanding of nonnative language learners' speech is a challenging problem. In this paper, we investigate the use of dialogue context cues to help improve concept error rate (CER) of nonnative speech in a language learning system. Given that the student's task is known, we show that incorporating the game scores to help select the best hypothesis improves the CER. We also introduce a novel N-best fusion method to create a single final hypothesis on the meaning level. The experimental results show that the fusion methods can further improve the CER.

Published in:

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Date of Conference:

25-30 March 2012