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Joint signal and model based noise matching noise robustness method for automatic speech recognition

机译:基于联合信号和模型的噪声匹配噪声鲁棒性自动语音识别方法

摘要

A noise robustness method operates jointly in a signal domain and a model domain. For example, energy is added in the signal domain for frequency bands where an actual noise level of an incoming signal is lower than a noise level used to train models, thus obtaining a compensated signal. Also, energy is added in the model domain for frequency bands where noise level of the incoming signal or the compensated signal is higher than the noise level used to train the models. Moreover, energy is never removed, thereby avoiding problems of higher sensitivity of energy removal to estimation errors.
机译:噪声鲁棒性方法在信号域和模型域中共同操作。例如,在输入信号的实际噪声水平低于用于训练模型的噪声水平的频带的信号域中添加能量,从而获得补偿信号。另外,在模型域中,对于输入信号或补偿信号的噪声电平高于用于训练模型的噪声电平的频带,会在模型域中添加能量。而且,从不去除能量,从而避免了能量去除对估计误差的更高敏感性的问题。

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