Detecting pitch accents at the word, syllable and vowel level
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Semi-supervised learning for automatic prosodic event detection using co-training algorithm
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Spoken emotion recognition through optimum-path forest classification using glottal features
Computer Speech and Language
N-best rescoring based on pitch-accent patterns
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Analysis of inconsistencies in cross-lingual automatic ToBI tonal accent labeling
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
Cross-lingual English Spanish tonal accent labeling using decision trees and neural networks
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
From English pitch accent detection to Mandarin stress detection, where is the difference?
Computer Speech and Language
Analysis of inter-transcriber consistency in the Cat_ToBI prosodic labeling system
Speech Communication
International Journal of Speech Technology
Characterization and recognition of emotions from speech using excitation source information
International Journal of Speech Technology
A fuzzy classifier to deal with similarity between labels on automatic prosodic labeling
Computer Speech and Language
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With the advent of prosody annotation standards such as tones and break indices (ToBI), speech technologists and linguists alike have been interested in automatically detecting prosodic events in speech. This is because the prosodic tier provides an additional layer of information over the short-term segment-level features and lexical representation of an utterance. As the prosody of an utterance is closely tied to its syntactic and semantic content in addition to its lexical content, knowledge of the prosodic events within and across utterances can assist spoken language applications such as automatic speech recognition and translation. On the other hand, corpora annotated with prosodic events are useful for building natural-sounding speech synthesizers. In this paper, we build an automatic detector and classifier for prosodic events in American English, based on their acoustic, lexical, and syntactic correlates. Following previous work in this area, we focus on accent (prominence, or ldquostressrdquo) and prosodic phrase boundary detection at the syllable level. Our experiments achieved a performance rate of 86.75% agreement on the accent detection task, and 91.61% agreement on the phrase boundary detection task on the Boston University Radio News Corpus.