title: Classification of stop place in consonant-vowel contexts using feature extrapolation of acoustic-phonetic features in telephone speech.
abstract: 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.
authors.affiliation: Department of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul, Korea 120-749. jaesuk2002@dsp.yonsei.ac.kr
authors.forename: Jung-Won
authors.initials: JW
authors.lastname: Lee
authors.forename: Jeung-Yoon
authors.initials: JY
authors.lastname: Choi
authors.forename: Hong-Goo
authors.initials: HG
authors.lastname: Kang
mesh_terms: D000465:Algorithms; D003463:Cues; D006801:Humans; D010700:Phonetics; D021641:Recognition, Psychology / Q000502:physiology*; D013018:Sound Spectrography; D013061:Speech Acoustics; D013067:Speech Perception / Q000502:physiology*; D049250:Speech Recognition Software; D013689:Telephone