Automatic scoring of pronunciation quality
Speech Communication
Prosody-based automatic segmentation of speech into sentences and topics
Speech Communication - Special issue on accessing information in spoken audio
Structural event detection for rich transcription of speech
Structural event detection for rich transcription of speech
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Detecting structural events for assessing non-native speech
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Assessment of ESL learners' syntactic competence based on similarity measures
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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We investigated using structural events, e.g., clause and disfluency structure, from transcriptions of spontaneous non-native speech, to compute features for measuring speaking proficiency. Using a set of transcribed audio files collected from the TOEFL Practice Test Online (TPO), we conducted a sophisticated annotation of structural events, including clause boundaries and types, as well as disfluencies. Based on words and the annotated structural events, we extracted features related to syntactic complexity, e.g., the mean length of clause (MLC) and dependent clause frequency (DEPC), and a feature related to disfluencies, the interruption point frequency per clause (IPC). Among these features, the IPC shows the highest correlation with holistic scores (r = -0.344). Furthermore, we increased the correlation with human scores by normalizing IPC by (1) MLC (r = -0.386), (2) DEPC (r = -0.429), and (3) both (r = -0.462). In this research, the features derived from structural events of speech transcriptions are found to predict holistic scores measuring speaking proficiency. This suggests that structural events estimated on speech word strings provide a potential way for assessing non-native speech.