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    • 3. 发明申请
    • Unsupervised incremental adaptation using maximum likelihood spectral transformation
    • 使用最大似然谱变换的无监督增量自适应
    • US20060009972A1
    • 2006-01-12
    • US11215415
    • 2005-08-30
    • Dongsuk YukDavid Lubensky
    • Dongsuk YukDavid Lubensky
    • G10L19/14
    • G10L15/065G10L15/20G10L21/0216
    • In a speech recognition system, a method of transforming speech feature vectors associated with speech data provided to the speech recognition system includes the steps of receiving likelihood of utterance information corresponding to a previous feature vector transformation, estimating one or more transformation parameters based, at least in part, on the likelihood of utterance information corresponding to a previous feature vector transformation, and transforming a current feature vector based on maximum likelihood criteria and/or the estimated transformation parameters, the transformation being performed in a linear spectral domain. The step of estimating the one or more transformation parameters includes the step of estimating convolutional noise Niα and additive noise Niβ for each ith component of a speech vector corresponding to the speech data provided to the speech recognition system.
    • 在语音识别系统中,将与提供给语音识别系统的语音数据相关联的语音特征矢量变换的方法包括以下步骤:接收与先前的特征向量变换相对应的话语信息的可能性,至少基于至少估计一个或多个变换参数 部分地基于与先前的特征向量变换相对应的发声信息的可能性,并且基于最大似然准则和/或估计的变换参数来变换当前特征向量,在线性频域中执行变换。 估计一个或多个变换参数的步骤包括以下步骤:估计卷积噪声N<α>和加性噪声N< 对应于提供给语音识别系统的语音数据的语音向量的每个第i个分量的