Computational methods can successfully syllabify English words
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Peer-reviewed literature demonstrates that computational approaches, such as support vector machines and probabilistic context-free grammars, can successfully and accurately syllabify English words.
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.
We present a phonological probabilistic context-free grammar, which describes the word and syllable structure of German words. The grammar is trained on a large corpus by a simple supervised method, and evaluated on a syllabification task achieving 96.88% word accuracy on word tokens, and 90.33% on word types. We added rules for English phonemes to the grammar, and trained the enriched grammar on an English corpus. Both grammars are evaluated qualitatively showing that probabilistic context-free grammars can contribute linguistic knowledge to phonology. Our formal approach is multilingual, while the training data is language-dependent.
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