A maximum entropy approach to natural language processing
Computational Linguistics
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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Structural event detection for rich transcription of speech
Structural event detection for rich transcription of speech
Automatic measurement of syntactic development in child language
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Towards using structural events to assess non-native speech
IUNLPBEA '10 Proceedings of the NAACL HLT 2010 Fifth Workshop on Innovative Use of NLP for Building Educational Applications
Utilizing cumulative logit models and human computation on automated speech assessment
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Vocabulary profile as a measure of vocabulary sophistication
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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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Structural events, (i.e., the structure of clauses and disfluencies) in spontaneous speech, are important components of human speaking and have been used to measure language development. However, they have not been actively used in automated speech assessment research. Given the recent substantial progress on automated structural event detection on spontaneous speech, we investigated the detection of clause boundaries and interruption points of edit disfluencies on transcriptions of non-native speech data and extracted features from the detected events for speech assessment. Compared to features computed on human-annotated events, the features computed on machine-generated events show promising correlations to holistic scores that reflect speaking proficiency levels.