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    • 66. 发明申请
    • Noise Suppressor for Robust Speech Recognition
    • 噪声抑制器用于强大的语音识别
    • US20100153104A1
    • 2010-06-17
    • US12335558
    • 2008-12-16
    • Dong YuLi DengYifan GongJian WuAlejandro Acero
    • Dong YuLi DengYifan GongJian WuAlejandro Acero
    • G10L15/20
    • G10L21/0208G10L15/20
    • Described is noise reduction technology generally for speech input in which a noise-suppression related gain value for the frame is determined based upon a noise level associated with that frame in addition to the signal to noise ratios (SNRs). In one implementation, a noise reduction mechanism is based upon minimum mean square error, Mel-frequency cepstra noise reduction technology. A high gain value (e.g., one) is set to accomplish little or no noise suppression when the noise level is below a threshold low level, and a low gain value set or computed to accomplish large noise suppression above a threshold high noise level. A noise-power dependent function, e.g., a log-linear interpolation, is used to compute the gain between the thresholds. Smoothing may be performed by modifying the gain value based upon a prior frame's gain value. Also described is learning parameters used in noise reduction via a step-adaptive discriminative learning algorithm.
    • 描述了通常用于语音输入的噪声降低技术,其中除了信噪比(SNR)之外,基于与该帧相关联的噪声电平来确定用于帧的噪声抑制相关增益值。 在一个实现中,降噪机制基于最小均方误差,Mel-frequency cepstra降噪技术。 设置高增益值(例如一个),以在噪声电平低于阈值低电平时实现很少或没有噪声抑制,以及设置或计算的低增益值,以实现高于阈值高噪声电平的大噪声抑制。 使用噪声功率相关函数,例如对数线性插值来计算阈值之间的增益。 可以通过基于先前帧的增益值修改增益值来执行平滑化。 还描述了通过步进自适应识别学习算法在降噪中使用的学习参数。