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This is a poster material of recent research accepted by IEEE ICASSP 2024.
Title: SELF-SUPERVISED SPEAKER VERIFICATION WITH ADAPTIVE THRESHOLD AND HIERARCHICAL TRAINING

For more inforamation, please check out the publication at IEEE Xplore:
https://ieeexplore.ieee.org/document/10448455

This paper focuses on the transition of automatic speaker verification systems from time delay neural networks (TDNN) to ResNet-based networks. TDNN-based systems use a statistics pooling layer to aggregate temporal information which is suitable for two-dimensional tensors. Even though ResNet-based models produce three-dimensional tensors, they continue to incorporate the statistics pooling layer.

An automatic, text-independent speaker verification (SV) system is proposed using Line Spectral Frequency (LSF) features. The state-of-the-art Gaussian Mixture Model with Universal Background Model (GMM-UBM) framework is used for speaker modeling and verification. A score-level fusion based technique is employed in order to extract complementary information from static and dynamic LSF features and improve the noise-robustness of the SV system. In addition, the speaker-discriminative power of different speech zones such as vowels, non-vowels, and transitions are investigated.