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Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification by Wang, Zhenyu; Xia, Wei; Hansen, John H. L. is a scholarly article available to read on EtoBox.

What is Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification about?

Forensic audio analysis for speaker verification offers unique challenges due to location/scenario uncertainty and diversity mismatch between reference and naturalistic field recordings. The lack of real naturalistic forensic audio corpora with ground-truth speaker identity represents a major challenge in this field. It is also difficult to directly employ small-scale domain-specific data to train complex neural network architectures due to domain mismatch and loss in performance. Alternatively, cross-domain speaker verification for multiple acoustic environments is a challenging task which could advance research in audio forensics. In this study, we introduce a CRSS-Forensics audio dataset collected in multiple acoustic environments. We pre-train a CNN-based network using the VoxCeleb data, followed by an approach which fine-tunes part of the high-level network layers with clean speech from CRSS-Forensics. Based on this fine-tuned model, we align domain-specific distributions in the embedding space with the discrepancy loss and maximum mean discrepancy (MMD). This maintains effective performance on the clean set, while simultaneously generalizes the model to other acoustic domains

Author

Wang, Zhenyu; Xia, Wei; Hansen, John H. L.

Published

2020

Language

EN