A spectral transition measure based MELCEPSTRAL features for obstruent detection

Obstruents are the key landmark events found in the speech signal. In this paper, we propose use of spectral transition measure (STM) to locate the obstruents in the continuous speech. The proposed approach does not take in to account any prior information (like phonetic sequence, speech transcription, and number of obstruents in the speech). Hence this approach is unsupervised and unconstraint approach. In this paper, we propose use of state-of-the-art Mel Frequency Cepstral Coefficients (MFCC)-based features to capture spectral transition for obstruent detection task. It is expected more spectral transition in the vicinity of obstruents. The entire experimental setup is developed on TIMIT database. The detection efficiency and estimated probability are around 77 % and 0.77 respectively (with 30 ms agreement duration and 0.4 STM threshold).