Syllabification by Phone Categorization

TL;DR

This paper introduces a novel, language-agnostic method for syllabification using phone categorization optimized by a genetic algorithm and a hidden Markov model, showing promising results on English words.

Contribution

It presents a new hybrid approach combining genetic algorithms and HMMs for syllable segmentation that is low-cost and language-independent.

Findings

Effective syllabification on English words

Hybrid genetic algorithm improves phone categorization

Promising preliminary results

Abstract

Syllables play an important role in speech synthesis, speech recognition, and spoken document retrieval. A novel, low cost, and language agnostic approach to dividing words into their corresponding syllables is presented. A hybrid genetic algorithm constructs a categorization of phones optimized for syllabification. This categorization is used on top of a hidden Markov model sequence classifier to find syllable boundaries. The technique shows promising preliminary results when trained and tested on English words.