Syllables are considered as basic supra-segmental units, used mainly
in prosodic modelling. It has long been thought that efficient syllabification
algorithms may also provide valuable cues for improved segmental (acoustic)
modelling. However, the best current syllabification methods work offline,
considering the power envelope of whole utterance.
In this paper we introduce a new method for detection of syllable boundaries based on a model of speech parsing into syllables by neural oscillations in human auditory cortex. Neural oscillations automatically lock to speech slow fluctuations that convey the syllabic rhythm. Similarly as humans encode speech incrementally, i.e., not considering future temporal context, the proposed method works incrementally as well. In addition, it is highly robust to noise. Syllabification performance for English and different noise conditions was compared to the existing Mermelstein and group delay algorithms. While the performance of the existing methods depend on the type of noise and signal to noise ratio, the performance of the proposed method is constant under all noise conditions.
Bibliographic reference. Hyafil, Alexandre / Cernak, Milos (2015): "Neuromorphic based oscillatory device for incremental syllable boundary detection", In INTERSPEECH-2015, 1191-1195.