A Deformable Convolution GAN Approach for Speech Dereverberation in Cochlear Implant Users

Speech dereverberation is crucial for enhancing intelligibility and quality, especially for cochlear implant (CI) users, who are highly susceptible to smearing effects induced by reverberation. While conventional and deep learning-based methods have shown promise for normal-hearing (NH) individuals, their effectiveness for CI users remains limited. To bridge this gap, we propose a deformable convolutional GAN architecture for dereverberation for CI users. The deformable convolution layers introduce kernel offset prediction, adaptively adjusting the receptive field based on distortion in reverberant speech. We first evaluate the effectiveness of the proposed method on REVERB challenge dataset. A listening test is conducted with both NH and CI users. Results show that the proposed method markedly improves speech intelligibility for CI users by preserving a more intact envelope structure, enhancing their ability to perceive key transient speech segments for sentence comprehension.