Xiaolong Li, Li Deng, Yun-Cheng Ju, and Alex Acero
This paper presents an Automatic Reading Tutoring (ART) system using state-of-the-art speech recognition technologies aimed to improve children’s oral reading ability. The features of this system include a compact and robust language model designed for detecting disfluencies in children’s speech, low-footprint implementation, and built-in microphone array. Our system is targeting on hand-held devices to provide better accessibility, flexibility, and freedom for children’s reading practice. The focus of this paper is on the current system’s architecture, which has achieved real-time performance on two hand-held, small-form-factor devices (UMPC and Motion Tablet), with the same detection rate and false alarm rate as on desktop PCs. We also report the latest effort on a prototype system running on a PDA (Windows Mobile 6).
In Proceedings of Interspeech
Publisher International Speech Communication Association
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