Syll-O-Matic: An adaptive time-frequency representation for the automatic segmentation of speech into syllables

Abstract

This paper introduces novel paradigms for the segmentation of speech into syllables. The main idea of the proposed method is based on the use of a time-frequency representation of the speech signal, and the fusion of intensity and voicing measures through various frequency regions for the automatic selection of pertinent information for the segmentation. The time-frequency representation is used to exploit the speech characteristics depending on the frequency region. In this representation, intensity profiles are measured to provide information into various frequency regions, and voicing profiles are measured to determine the frequency regions that are pertinent for the segmentation. The proposed method outperforms conventional methods for the detection of syllable landmark and boundaries on the TIMIT database of American-English, and provides a promising paradigm for the segmentation of speech into syllables.