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Convolutional neural network (CNN) modules are widely being used to buildhigh-end speech enhancement neural models. However, the feature extractionpower of vanilla CNN modules has been limited by the dimensionality constraintof the convolution kernels that are integrated - thereby, they have limitationsto adequately model the noise context information at the feature extractionstage. To this end, adding recurrency factor into the feature extracting CNNlayers, we introduce a robust context-aware feature extraction strategy forsingle-channel speech enhancement. As shown, adding recurrency results incapturing the local statistics of noise attributes at the extracted featureslevel and thus, the suggested model is effective in differentiating speech cueseven at very noisy conditions. When evaluated against enhancement models usingvanilla CNN modules, in unseen noise conditions, the suggested model withrecurrency in the feature extraction layers has produced a segmental SNR (SSNR)gain of up to 1.5 dB, an improvement of 0.4 in subjective quality in the MeanOpinion Score scale, while the parameters to be optimized are reduced by 25%.

A fully recurrent feature extraction for single channel speech enhancement