Enhancing speech recognition in fast-paced educational games using contextual cues

Arcade-style games like Tetris and Pacman are often difficult to adapt for educational purposes because their fast-paced intensity and keystroke-heavy nature leave little room for simultaneous practice of other skills. Incorporating spoken language technology could make it possible for players to learn as they play, keeping up with game speed through multimodal interaction. To date, however, it remains exceedingly difficult to augment fast-paced games with speech interaction because the frustrating effect of recognition errors highly compromises entertainment. In this paper, we design a modified version of Tetris with speech recognition to help students practice and remember word-picture mappings. Using utterances collected from learners interacting with the speechenabled Tetris game, we present and evaluate several techniques for leveraging contextual cues to increase recognition accuracy in fast-paced game environments.