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

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.