| Paper ID | F-3-3.2 |
| Paper Title |
Robust Speech Dereverberation Based on WPE and Deep Learning |
| Authors |
Hao Li, Inner Mongolia University, China; Xueliang Zhang, Guanglai Gao, Professor, China |
| Session |
F-3-3: Signal Processing Systems for AI |
| Time | Thursday, 10 December, 17:30 - 19:30 |
| Presentation Time: | Thursday, 10 December, 17:45 - 18:00 Check your Time Zone |
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All times are in New Zealand Time (UTC +13) |
| Topic |
Signal Processing Systems: Design and Implementation (SPS): |
| Abstract |
Reverberation has a considerable impacts on speech quality and intelligibility. Weighted prediction error (WPE) employs a linear regression model to estimate late reverberation and then cancel it. The key point of the WPE is to estimate the power spectrum of the early speech. However, its estimation relies on an iterative procedure with high computational complexity. Another problem is that the WPE has a noise-free assumption. So, the performance degrades in noisy conditions. To address these problems, we propose an algorithm for speech dereverberation in the presence of background noise, in which deep learning is integrated into the WPE method. Specifically, we employ a neural network to predict the power spectral density (PSD) of early speech and a binary mask which distinguishes target speech from background noise. To alleviate the noise impact on estimation of echo path, a dual-filter strategy is adopted to model the echo paths of target speech and background noise individually. Experimental results show that the proposed method significantly improves speech quality in noisy environments. |