Instance-Based Learning Algorithms
Machine Learning
Novel-word pronunciation: a cross-language study
Speech Communication - Speech science and technology: a selection from the papers presented at the Fourth International Conference in Speech Science and Technology (SST-92)
Forgetting Exceptions is Harmful in Language Learning
Machine Learning - Special issue on natural language learning
Speaking in shorthand — a syllable-centric perspective for understanding pronunciation variation
Speech Communication - Special issue on modeling pronunciation variation for automatic speech recognition
Artificial Intelligence Review - Special issue on lazy learning
A multistrategy approach to improving pronunciation by analogy
Computational Linguistics
Finite state methods for hyphenation
Natural Language Engineering
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
Multilingual pronunciation by analogy
Natural Language Engineering
Computer Speech and Language
On the syllabification of phonemes
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
A ranking approach to stress prediction for letter-to-phoneme conversion
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 1 - Volume 1
Letter-phoneme alignment: an exploration
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
PROPOR'06 Proceedings of the 7th international conference on Computational Processing of the Portuguese Language
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In spite of difficulty in defining the syllable unequivocally, and controversy over its role in theories of spoken and written language processing, the syllable is a potentially useful unit in several practical tasks which arise in computational linguistics and speech technology. For instance, syllable structure might embody valuable information for building word models in automatic speech recognition, and concatenative speech synthesis might use syllables or demisyllables as basic units. In this paper, we first present an algorithm for determining syllable boundaries in the orthographic form of unknown words that works by analogical reasoning from a database or corpus of known syllabifications. We call this syllabification by analogy (SbA). It is similarly motivated to our existing pronunciation by analogy (PbA) which predicts pronunciations for unknown words (specified by their spellings) by inference from a dictionary of known word spellings and corresponding pronunciations. We show that including perfect (according to the corpus) syllable boundary information in the orthographic input can dramatically improve the performance of pronunciation by analogy of English words, but such information would not be available to a practical system. So we next investigate combining automatically-inferred syllabification and pronunciation in two different ways: the series model in which syllabification is followed sequentially by pronunciation generation; and the parallel model in which syllabification and pronunciation are simultaneously inferred. Unfortunately, neither improves performance over PbA without syllabification. Possible reasons for this failure are explored via an analysis of syllabification and pronunciation errors.