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In Section 3, we adopt a DNN-based speech dereverberation module as the pre-processor for acoustic modeling.
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Avendano et al. [5, 6] explored a speech dereverberation technique for which the principle was the recovery of the envelope modulations of the original (anechoic) speech.
Fig. 2 DNN-based speech dereverberation.
Since in theory they can achieve perfect dereverberation [19], they represent an attractive approach to speech dereverberation.
In this study, we extend our previous work on DNN-based speech dereverberation [29].
The frame-level alignment requirement can be easily satisfied in speech dereverberation tasks.
The well-trained speech dereverberation DNN will be used as a pre-processor for acoustic modeling.
A novel approach for multimicrophone speech dereverberation was proposed in [8].
A novel approach for multi-microphone speech dereverberation was proposed in [14].
Han et al. [19] implemented the supervised learning approach based on DNNs to perform speech dereverberation.
Inverse filtering [13] is one of the commonly used single-channel speech dereverberation techniques.
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