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Entanglement of speaker features and redundant features may lead to poor performance when evaluating speaker verification systems on an unseen domain. To address this issue, we propose an InfoMax domain separation and adaptation network (InfoMax–DSAN) to disentangle the domain-specific features and domain-invariant speaker features based on domain adaptation techniques. A frame-based mutual information neural estimator is proposed to maximize the mutual information between frame-level features and input acoustic features, which can help retain more useful information.

State-of-the-art speaker verification systems take frame-level acoustics features as input and produce fixed-dimensional embeddings as utterance-level representations. Thus, how to aggregate information from frame-level features is vital for achieving high performance. This paper introduces short-time spectral pooling (STSP) for better aggregation of frame-level information. STSP transforms the temporal feature maps of a speaker embedding network into the spectral domain and extracts the lowest spectral components of the averaged spectrograms for aggregation.