Detection of obstruent consonant landmark for knowledge based speech recognition

Obstruent consonant landmarks are detected using spectral energy difference profiles. This study expands upon previous work by Liu. A[J. Acoust. Soc. Am. 100, 3417‐3430, 1996]. The proposed algorithm detects four types of landmarks : [stop closure], [stop release], [fricative closure] and [fricative release], where affricates are detected by combining [stop closure], [fricative closure] and [fricative release]. In addition to finding abrupt changes in energy differences, we use energy contours, relative energy and spectral center of gravity differences. This method results in improved performance particularly for CV obstruents. Overall detection rates for stop closure and release are 76.9% and 85.7% for obstruent landmarks in TIMIT, and fricatives yield 82.2% and 83.6% respectively. For strident fricatives, the figures are 94.7% and 93.6%.