Dynamic Convolution With Global-Local Information for Session-Invariant Speaker Representation Learning

Various mismatchedconditions result in performance degradation of the speaker verification (SV) systems. To address this issue, we extract robust speaker representations by devising a global-local information-based dynamic convolution neural network. In the proposed method, both global and local information of the input features are exploited to dynamically modify the convolution kernel values. This increases the model capability of capturing speaker characteristics by compensating both the inter- and intra-session variabilities. Extensive experiments on four publicly available SV datasets show significant and consistent improvements over the conventional approaches. The effectiveness of the proposed method is further investigated using ablation studies and visualizations.