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Supervised Learning of Response Grammars in a Spoken Call System

Presented at Leipzig (Germany)
Publication date2015
Abstract

We summarise experiments carried out using a system-initiative spoken CALL system, in which permitted responses to prompts are defined using a minimal formalism based on templates and regular expressions, and describe a simple structural learning algorithm that uses annotated data to update response definitions. Using 1 927 utterances of training data, we obtained a relative improvement of 20% in the system's ability to react differentially to correct and incorrect input, measured on a previously unseen test set. The results are significant at p < 0:005.

Citation (ISO format)
RAYNER, Emmanuel et al. Supervised Learning of Response Grammars in a Spoken Call System. In: Workshop on Speech and Language Technology in Education (SLaTE). [s.l.] : [s.n.], 2015.
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  • PID : unige:73662
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