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Related Experiment Video

Updated: Jun 24, 2026

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Published on: September 27, 2024

Automatic syllabification in English: a comparison of different algorithms.

Yannick Marchand1, Connie R Adsett, Robert I Damper

  • 1Institute for Biodiagnostics (Atlantic), National Research Council Canada.

Language and Speech
|April 2, 2009
PubMed
Summary

Data-driven methods, particularly syllabification by analogy (SbA), significantly outperform rule-based systems for automatic word syllabification. Syllabification is easier using pronunciation than spelling data.

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Published on: July 13, 2019

Area of Science:

  • Computational Linguistics
  • Natural Language Processing
  • Phonology

Background:

  • Automatic syllabification is complex due to the ambiguous definition of a syllable.
  • Existing approaches include rule-based and data-driven methods, but no standard algorithm is widely accepted.
  • Evaluating syllabification accuracy is challenging due to the lack of a definitive gold standard.

Purpose of the Study:

  • To compare the performance of rule-based and data-driven automatic syllabification algorithms.
  • To investigate the impact of using consensus data from multiple lexicons for training and evaluation.
  • To determine the most effective approach for automatic syllabification.

Main Methods:

  • Compared two rule-based approaches (Hammond's and Fisher's implementation of Kahn's) and three data-driven techniques (look-up, exemplar-based generalization, and syllabification by analogy - SbA).
  • Utilized three databases derived from two independent lexicons, including a consensus lexicon of 13,594 words.
  • Evaluated performance based on word and juncture accuracies in both pronunciation and spelling domains.

Main Results:

  • Data-driven techniques significantly outperformed rule-based systems in accuracy.
  • Syllabification based on pronunciation data yielded better results than spelling data.
  • Syllabification by Analogy (SbA) consistently achieved the best performance across all databases.

Conclusions:

  • Data-driven methods, especially SbA, are superior to rule-based systems for automatic syllabification.
  • Pronunciation-based syllabification is more tractable than spelling-based syllabification.
  • Consensus data from multiple lexicons improves the robustness of syllabification evaluation and training.