On the syllabification of phonemes

Authors:
Susan Bartlett;Grzegorz Kondrak;Colin Cherry
Affiliations:
University of Alberta, Edmonton, AB, Canada;University of Alberta, Edmonton, AB, Canada;Microsoft Research, One Microsoft Way, Redmond, WA
Venue:
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2009

Citing 8
Cited 4

IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms

Artificial Intelligence Review - Special issue on lazy learning
Support vector machine learning for interdependent and structured output spaces

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Automatic detection of syllable boundaries combining the advantages of treebank and bracketed corpora training

ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Inducing probabilistic syllable classes using multivariate clustering

ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Probabilistic context-free grammars for phonology

MPL '02 Proceedings of the ACL-02 workshop on Morphological and phonological learning - Volume 6
Can syllabification improve pronunciation by analogy of English?

Natural Language Engineering
Improving syllabification models with phonotactic knowledge

SIGPHON '06 Proceedings of the Eighth Meeting of the ACL Special Interest Group on Computational Phonology and Morphology
Representational bias in unsupervised learning of syllable structure

CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning

Tagging and linking web forum posts

CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
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
Automatic prosodic event detection using a novel labeling and selection method in co-training

Speech Communication
Modeling improved syllabification algorithm for Amharic

Proceedings of the International Conference on Management of Emergent Digital EcoSystems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Syllables play an important role in speech synthesis and recognition. We present several different approaches to the syllabification of phonemes. We investigate approaches based on linguistic theories of syllabification, as well as a discriminative learning technique that combines Support Vector Machine and Hidden Markov Model technologies. Our experiments on English, Dutch and German demonstrate that our transparent implementation of the sonority sequencing principle is more accurate than previous implementations, and that our language-independent SVM-based approach advances the current state-of-the-art, achieving word accuracy of over 98% in English and 99% in German and Dutch.