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    • 1. 发明授权
    • Method and apparatus for a robust feature extraction for speech recognition
    • 用于语音识别的鲁棒特征提取的方法和装置
    • US06678657B1
    • 2004-01-13
    • US09694617
    • 2000-10-23
    • Raymond BrücknerHans-Günter HirschRainer KlischVolker Springer
    • Raymond BrücknerHans-Günter HirschRainer KlischVolker Springer
    • G10L1502
    • G10L15/20G10L15/02G10L21/0208
    • The present invention relates to a method and an apparatus for a robust feature extraction for speech recognition in a noisy environment, wherein the speech signal is segmented and is characterized by spectral components. The speech signal is splitted into a number of short term spectral components in L subbands, with L=1, 2, . . . and a noise spectrum from segments that only contain noise is estimated. Then a spectral subtraction of the estimated noise spectrum from the corresponding short term spectrum is performed and a probability for each short term spectrum component to contain noise is calculated. Finally these spectral component of each short-term spectrum, having a low probability to contain speech are interpolated in order to smooth those short-term, spectra that only contain noise. With the interpolation the spectral components containing noise are interpolated by reliable spectral speech components that could be found in the neighborhood.
    • 本发明涉及一种用于在噪声环境中进行语音识别的鲁棒特征提取的方法和装置,其中语音信号被分段并且由频谱分量表征。 语音信号分为L个子带中的多个短期频谱分量,其中L = 1,2。 。 。 并且估计仅包含噪声的段的噪声频谱。 然后,对相应的短期频谱进行估计噪声频谱的频谱减法,并计算每个短期频谱分量包含噪声的概率。 最后,对包含语音的低概率的每个短期频谱的这些频谱分量进行插值,以平滑那些仅包含噪声的短期谱图。 通过插值,包含噪声的频谱成分被可靠的光谱语音分量内插,该分量可以在邻域中找到。