Syllable-based segment-hypotheses generation in fluently spoken speech using gross articulatory features

Syllable based segmentation systems using the energy contour approach are limited to carefully uttered speech. This paper describes a syllable based acoustic-phonetic frontend incorporating knowledge of articulatory aspects of syllable production, in order to detect syllabic nuclei in quickly uttered speech with high reliability. In an iterative procedure hypotheses for syllabic nuclei and their phonetic context are established, using robust gross articulatory classes and features. Remaining intersyllabic consonant clusters are segmented into initial- and final consonant clusters. Due to unavoidable acoustic-phonetic ambiguities alternative segmentations are permitted and several weighted hypotheses for initial-and final consonant clusters are produced.