Robust Vowel Landmark Detection Using Epoch-Based Features

Automatic detection of vowel landmarks is useful in many applications such as automatic speech recognition (ASR), audio search, syllabification of speech and expressive speech processing. In this paper, acoustic features extracted around epochs are proposed for detection of vowel landmarks in continuous speech. These features are based on zero frequency filtering (ZFF) and single frequency filtering (SFF) analyses of speech. Excitation source based features are extracted using ZFF method and vocal tract system based features are extracted using SFF method. Based on these features, a rule-based algorithm is developed for vowel landmark detection (VLD). Performance of the proposed VLD algorithm is studied on three different databases namely, TIMIT (read), NTIMIT (channel degraded) and Switchboard corpus (conversational speech). Results show that the proposed algorithm performs equally well compared to state-of-the-art techniques on TIMIT and better on NTIMIT and Switchboard corpora. Proposed algorithm also displays consistent performance on TIMIT and NTIMIT datasets for different levels of noise degradations.