AUTOMATED DETECTION OF SPEECH LANDMARKS USI NG GAUSSIAN MIXTURE MODELING
A. R. Jayan, Prem C. Pandey · 2008
Landmarks in speech signal are regions with a brupt spectral variations. Automated detection of these regions is important for several applications in speech processing. Performance of landmark detection using parameters extracted from predefined spectral bands generally gets limited by speaker related spectral variability. This paper presents a landmark detection technique which adapts to the acoustic properties of speech. Parameters are extracted from Gaussian mixture modeling (GMM) of smoothed spectral envelope. A single rate of rise function, obtained from the set of GMM parameters, is used for locating landmark regions. The method was evaluated using manually labeled VCV syllables and sentences. It was possible to detect 85 % of stop release bursts in VCV syllables and 82 % in sentences, with an accuracy of 5 ms, compared to the manually located landmarks.