To address the challenge of mapping characteristics of individuals’ speech to information about the group, we coded behavioral and learning-related indicators of collaboration at the individual level.
This work addresses lexical unit discovery for languages without (usable) written resources.
We introduce the SRI speech-based collaborative learning corpus, a novel collection designed for the investigation and measurement of how students collaborate together in small groups.
This work investigates whether nonlexical information from speech can automatically predict the quality of small-group collaborations. Audio was collected from students as they collaborated in groups of three to solve math problems.
This study investigates whether automatic audio- based monitoring of interactions can predict collaboration quality.
Classification of Lexical Stress Using Spectral and Prosodic Features for Computer-assisted Language Learning Systems
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.
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.
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.
We introduce a new database for evaluation of speaker recognition systems.