Robust Acoustic-Based Syllable Detection

In this paper, we describe a method to detect syllabic nuclei in continuous speech. It employs two basic and robust acoustic features, periodicity and energy, to detect syllable landmarks. This method is evaluated on TIMIT, noise additive TIMIT and NTIMIT datasets with typical total error rates of around 30% in all the datasets, except for extremely adverse 0dB signal-noise-ratio environments, while HMM-based systems degrade rigorously. Based on the landmarks, a vowel classifier is further constructed and achieves the same performance as HMM-based systems.