Recognizing Young Readers' Spoken Questions

Article type: Research Article

Authors: Chen, Wei | Mostow, Jack | Aist, Gregory

Affiliations: Project LISTEN, School of Computer Science, Carnegie Mellon University, USA. [email protected] | Communication Studies and Applied Linguistics, Department of English, Iowa State University, USA

Abstract: Free-form spoken input would be the easiest and most natural way for young children to communicate to an intelligent tutoring system. However, achieving such a capability poses a challenge both to instruction design and to automatic speech recognition. To address the difficulties of accepting such input, we adopt the framework of predictable response training, which aims at simultaneously achieving linguistic predictability and educational utility. We design instruction in this framework to teach children the reading comprehension strategy of self-questioning. To filter out some misrecognized speech, we combine acoustic confidence with language modeling techniques that exploit the predictability of the elicited responses. Compared to a baseline that does neither, this approach performs significantly better in concept recall (47% vs. 28%) and precision (61% vs. 39%) on 250 unseen utterances from 34 previously unseen speakers. We conclude with some design implications for future speech enabled tutoring systems.

Keywords: Intelligent tutoring system, spoken dialogue system, language modeling, question generation, predictable response training, children's reading, comprehension strategy instruction, Project LISTEN's Reading Tutor

DOI: 10.3233/JAI-130031

Journal: International Journal of Artificial Intelligence in Education, vol. 21, no. 4, pp. 255-269, 2013