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    • 62. 发明申请
    • METHOD AND SYSTEM TO IDENTIFY HUMAN CHARACTERISTICS USING SPEECH ACOUSTICS
    • 使用语音呼叫识别人类特征的方法和系统
    • WO2015123332A1
    • 2015-08-20
    • PCT/US2015/015465
    • 2015-02-11
    • BEGEL, Daniel
    • BEGEL, Daniel
    • G10L21/00
    • G10L17/26G10L25/63G10L25/66
    • The invention that is described herein identifies human characteristics by means of speech acoustics. It identifies acoustic transformational structures that are contained in speech and determines the best fit between these structures and classified behaviors. It also determines the best fit between the structures of unclassified speech and the structures of speech previously classified as representing a human characteristic, in order to discern the presence of that characteristic in the human token associated with the unclassified sample. The invention is useful for identifying a wide variety of cognitive, emotional, linguistic, behavioral, and existential human characteristics.
    • 本文所描述的本发明通过语音声学识别人的特征。 它识别语音中包含的声学变换结构,并确定这些结构和分类行为之间的最佳拟合。 它还确定未分类语音的结构与先前分类为表示人类特征的语音结构之间的最佳拟合,以便辨别与未分类样本相关联的人类令牌中的该特征的存在。 本发明对于识别各种认知,情感,语言,行为和存在的人的特征是有用的。
    • 64. 发明申请
    • CEPSTRAL SEPARATION DIFFERENCE
    • CEPSTRAL分离差异
    • WO2013187826A2
    • 2013-12-19
    • PCT/SE2013/050648
    • 2013-06-05
    • JEMARDATOR AB
    • KHAN, TahaWESTIN, JerkerDAUGHERTY, Mark
    • G10L25/66
    • G10L21/06G10L15/02G10L19/02G10L25/03G10L25/60G10L25/66
    • A method for characterization of a human speech comprises performing (220) of a discrete transform on a speech sample of the human speech. A speech logarithmic power spectrum is created (222) by taking a logarithmic of the speech frequency spectrum. An inverse discrete transform is performed (224) on the speech logarithmic power spectrum into the quefrency domain. Lifterings (226, 228) of the speech cepstrum is performed, giving a high and low end speech cepstrum, respectively. The discrete transform is performed (230) on the high end speech cepstrum, creating a source excitation log-power spectrum. The discrete transform is performed (232) on the low end speech cepstrum, creating a vocal tract filter log-power spectrum. A cepstral separation difference is calculated (234) as a difference between the source excitation log-power spectrum and the vocal tract filter log-power spectrum. The human speech is characterized (238) based on the cepstral separation difference.
    • 用于表征人类语音的方法包括对人类语音的语音样本执行离散变换(220)。 通过采用语音频谱的对数来创建语音对数功率谱(222)。 在语音对数功率谱上执行逆离散变换(224)到排队域中。 执行语音倒谱的升降(226,228),分别给出高低端语音倒频谱。 在高端语音倒频谱上执行离散变换(230),创建源激励对数功率谱。 在低端语音倒频谱上执行离散变换(232),创建声道滤波器对数功率谱。 计算倒谱分离差(234)作为源激发对数功率谱与声道滤波器对数功率谱之间的差异。 人类语言的特征(238)基于倒谱分离差异。