Convolutional Neural Networks to Enhance Coded Speech

Enhancing coded speech suffering from far-end acoustic background noise, quantization noise, and potentially transmission errors is a challenging task. In this paper, we propose two postprocessing approaches applying convolutional neural networks either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time-domain approach follows an end-to-end fashion, whereas the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves perceptual evaluation of speech quality by up to 0.25 mean opinion score listening quality objective points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec. In a subjective comparison category rating listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to legacy G.711, even better than uncoded speech with statistical significance. The source code for the cepstral domain approach to enhance G.711-coded speech is made available.
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https://github.com/ifnspaml/Enhancement-Coded-Speech.