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This work addresses lexical unit discovery for languages without (usable) written resources. Previous work has addressed this problem using entirely unsupervised methodologies. Our approach in contrast investigates the use of linguistic and speaker knowledge which are often available even if text resources are not. We create a framework that benefits from such resources, not assuming orthographic...
We present a system for detection of lexical stress in English words spoken by English learners. This system was designed to be part of the EduSpeak® computer-assisted language learning (CALL) software. The system uses both prosodic and spectral features to detect the level of stress (unstressed, primary or secondary) for each syllable in a word. Features are computed on the vowels and include normalized...
We present a system for detecting lexical stress in English words spoken by English learners. The system uses both spectral and segmental features to detect three levels of stress for each syllable in a word. The segmental features are computed on the vowels and include normalized energy, pitch, spectral tilt and duration measurements. The spectral features are computed at the frame level and are...
In the context of computer-aided language learning, automatic detection of specific phone mispronunciations by nonnative speakers can be used to provide detailed feedback about specific pronunciation problems. In previous work we found that significant improvements could be achieved, compared to standard approaches that compute posteriors with respect to native models, by explicitly modeling both...
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