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Acoustic beamforming has played a key role in the robust automatic speech recognition (ASR) applications. Accurate estimates of the speech and noise spatial covariance matrices (SCM) are crucial for successfully applying the minimum variance distortionless response (MVDR) beamforming. Reliable estimation of time-frequency (TF) masks can improve the estimation of the SCMs and significantly improve...
In this paper, we propose a feature adaptation method that combines speech features from multiple microphone channels for robust automatic speech recognition (ASR). The proposed method first transforms the features in all channels using channel-dependent linear transforms, and then sum the channels into one channel for acoustic modeling. The transform parameters are estimated by maximizing the likelihood...
This paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature mapping approach, DNN is trained from parallel clean/distorted speech corpus to map reverberant and noisy speech coefficients (such as log magnitude spectrum) to the underlying clean speech...
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