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Speech super-resolution/Bandwidth Extension (BWE) can improve downstreamtasks like Automatic Speaker Verification (ASV). We introduce a simple noveltechnique called Self-FiLM to inject self-supervision into existing BWE modelsvia Feature-wise Linear Modulation. We hypothesize that such informationcaptures domain/environment information, which can give zero-shotgeneralization. Self-FiLM Conditional GAN (CGAN) gives 18% relative improvementin Equal Error Rate and 8.5% in minimum Decision Cost Function usingstate-of-the-art ASV system on SRE21 test. We further by 1) deep feature lossfrom time-domain models and 2) re-training of data2vec 2.0 models onnaturalistic wideband (VoxCeleb) and telephone data (SRE Superset etc.).Lastly, we integrate self-supervision with CycleGAN to present a completelyunsupervised solution that matches the semi-supervised performance.

Self-FiLM: Conditioning GANs with self-supervised representations for bandwidth extension based speaker recognition