Spoken English assessment system for non-native speakers using acoustic and prosodic features

The absence of real-time and targeted feedback is often critical in spoken foreign language learning. Computer-assisted language assessment systems are playing an ever more important role in this domain. This work considers the idiosyncratic pronunciation patterns of Chinese English speakers and uses both acoustic and prosody features to capture pronunciation, word stress, and rhythm information. The proposed system uses a. automatic speech recognition and alignment for pronunciation assessment, b. a set of special features with appropriate normalization for word stress detection, and c. a prosody phrase prediction model for rhythm assessment; and is shown to give immediate and accurate analyses to speakers to improve learning efficiency.