Modeling and Automatic Detection O for Computer-assisted English Pr

We address sentence-level stress detection of English for Computer-Assisted Language Learning (CALL) by Japanese students. Stress models are set up by considering syllable structure and position of the syllable in a phrase, which will provide diagnostic information for students. We also propose a two-stage recognition method that first detects the presence of stress and then identifies the stress level using different weighted combinations of acoustic features. The modeling is coherent with conventional linguistic observations. The method achieves stress recognition rate of 95.1% for native and 84.1% for Japanese speakers.