Knowledge-based speech recognition systems extract acoustic cues from the signal to identify speech characteristics. For channel-deteriorated telephone speech, acoustic cues, especially those for stop consonant place, are expected to be degraded or absent. To investigate the use of knowledge-based methods in degraded environments, feature extrapolation of acoustic-phonetic features based on Gaussian mixture models is examined. This process is applied to a stop place detection module that uses burst release and vowel onset cues for consonant-vowel tokens of English. Results show that classification performance is enhanced in telephone channel-degraded speech, with extrapolated acoustic-phonetic features reaching or exceeding performance using estimated Mel-frequency cepstral coefficients (MFCCs). Results also show acoustic-phonetic features may be combined with MFCCs for best performance, suggesting these features provide information complementary to MFCCs.
Classification of stop place in consonant-vowel contexts using feature extrapolation of acoustic-phonetic features in telephone speech
Jung-Won Lee, Jeung-Yoon Choi, Hong-Goo Kang; Classification of stop place in consonant-vowel contexts using feature extrapolation of acoustic-phonetic features in telephone speech. J. Acoust. Soc. Am. 1 February 2012; 131 (2): 1536–1546. https://doi.org/10.1121/1.3672706
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