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Convolutional neural networks (CNN) and Transformer have wildly succeeded inmultimedia applications. However, more effort needs to be made to harmonizethese two architectures effectively to satisfy speech enhancement. This paperaims to unify these two architectures and presents a Parallel Conformer forspeech enhancement. In particular, the CNN and the self-attention (SA) in theTransformer are fully exploited for local format patterns and global structurerepresentations. Based on the small receptive field size of CNN and the highcomputational complexity of SA, we specially designed a multi-branch dilatedconvolution (MBDC) and a self-channel-time-frequency attention (Self-CTFA)module. MBDC contains three convolutional layers with different dilation ratesfor the feature from local to non-local processing. Experimental results showthat our method performs better than state-of-the-art methods in mostevaluation criteria while maintaining the lowest model parameters.

PCNN: A Lightweight Parallel Conformer Neural Network for Efficient Monaural Speech Enhancement