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    • 3. 发明公开
    • TONE FEATURES FOR SPEECH RECOGNITION
    • Tonale的特征的语音识别
    • EP1145225A1
    • 2001-10-17
    • EP00987248.2
    • 2000-11-10
    • Koninklijke Philips Electronics N.V.
    • HUANG, Chang-HanSEIDE, Frank
    • G10L15/18
    • G10L15/1807G10L25/15G10L2025/935
    • Robust acoustic tone features are achieved first by the introduction of on-line, look-ahead trace back of the fundamental frequency (F0) contour with adaptive pruning, this fundamental frequency serves as the signal preprocessing front-end. The F0 contour is subsequently decomposed into lexical tone effect, phrase intonation effect, and random effect by means of time-variant, weighted moving average (MA) filter in conjunction with weighted (placing more emphasis on vowels) least squares of the F0 contour. The intonation effect is removed by subtraction of the F0 contour under superposition assumption. The acoustic tone features are defined as two parts. First, is the coefficients of the second order weighted regression of the de-intonation of the F0 contour over neighbouring frames. The second part deals with the degree of the periodicity of the signal, which are the coefficients of the second order regression of the auto-correlation. These weights of the second order weighted regression of the de-intonation of the F0 contour are designed to emphasize/de-emphasize the voiced/unvoiced segments of the pitch contour in order to preserve the voiced pitch contour for the semi-voiced consonants.