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PhiNet, a speaker verification network, is introduced to enhance transparency by incorporating phonetic evidence into its decision-making process, mirroring forensic speaker comparison techniques. It provides phonetic-level comparisons for manual inspection and explicit reasoning for developers, enabling better error tracing and hyperparameter tuning. Experiments on VoxCeleb, SITW, and LibriSpeech show that PhiNet achieves comparable performance to black-box ASV models while providing interpretable explanations.
Finally, a speaker verification system that doesn't just tell you *if* two speakers match, but *why*, opening the door for more accountable and transparent voice authentication.
Despite remarkable progress, automatic speaker verification (ASV) systems typically lack the transparency required for high-accountability applications. Motivated by how human experts perform forensic speaker comparison (FSC), we propose a speaker verification network with phonetic interpretability, PhiNet, designed to enhance both local and global interpretability by leveraging phonetic evidence in decision-making. For users, PhiNet provides detailed phonetic-level comparisons that enable manual inspection of speaker-specific features and facilitate a more critical evaluation of verification outcomes. For developers, it offers explicit reasoning behind verification decisions, simplifying error tracing and informing hyperparameter selection. In our experiments, we demonstrate PhiNet鈥檚 interpretability with practical examples, including its application in analyzing the impact of different hyperparameters. We conduct both qualitative and quantitative evaluations of the proposed interpretability methods and assess speaker verification performance across multiple benchmark datasets, including VoxCeleb, SITW, and LibriSpeech. Results show that PhiNet achieves performance comparable to traditional black-box ASV models while offering meaningful, interpretable explanations for its decisions, bridging the gap between ASV and forensic analysis.
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