We present a method to remove unknown convolutive noise introduced to speech by reverberations of recording environments, utilizing some amount of training speech data from the reverberant environment, and any available non-reverberant speech data. Using Fourier transform computed over long temporal windows, which ideally cover the entire room impulse response, we convert room induced convolution to additions in the log spectral domain. Next, we compute a spectral normalization vector from statistics gathered over reverberated as well as over clean speech in the log spectral domain. During operation, this normalization vectors are used to alleviate reverberations from complex speech spectra recorded under the same reverberant conditions . Such dereverberated complex speech spectra are used to compute complex FDLP-spectrograms for use in automatic speech recognition.

本文介绍一种从混响环境中获取一定数量的训练音频数据，并利用这些数据以及其他可用非混响音频数据来消除混响噪声的方法。利用长时间窗口计算傅里叶变换，将房间产生的卷积转换为频率域的加法，并通过采集混响和干净音频数据的统计信息计算出一种用于规范化音频信号的向量，以解决相同混响条件下的抗混响问题。

机器语音识别的盲信号去混响技术