Pitch accent in context: predicting intonational prominence from text
Artificial Intelligence - Special volume on natural language processing
Effects of speaking rate and word frequency on pronunciations in conversational speech
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Estimators for stochastic "Unification-Based" grammars
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Modeling local context for pitch accent prediction
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Exponential families for conditional random fields
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Chinese prosody generation based on C-ToBI representation for text-to-speech
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
From English pitch accent detection to Mandarin stress detection, where is the difference?
Computer Speech and Language
C-TOBI-Based pitch accent prediction using maximum-entropy model
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
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The detection of prosodic characteristics is an important aspect of both speech synthesis and speech recognition. Correct placement of pitch accents aids in more natural sounding speech, while automatic detection of accents can contribute to better word-level recognition and better textual understanding. In this paper we investigate probabilistic, contextual, and phonological factors that influence pitch accent placement in natural, conversational speech in a sequence labeling setting. We introduce Conditional Random Fields (CRFs) to pitch accent prediction task in order to incorporate these factors efficiently in a sequence model. We demonstrate the usefulness and the incremental effect of these factors in a sequence model by performing experiments on hand labeled data from the Switchboard Corpus. Our model outperforms the baseline and previous models of pitch accent prediction on the Switch-board Corpus.