Explainable AI in Speaker Recognition -- Attention Map Visualisation and Evaluation
Explaining and understanding the decision-making process of artificial intelligence (AI) systems, particularly those implemented by neural networks, falls within the field of explainable AI (XAI). Analogous to the human attention mechanism, neural networks are assumed to possess their own attention mechanisms that selectively process information during decision-making. This work proposes to study one XAI topic: analysing and visualising the attention mechanisms of neural networks. Our experiments are performed on speaker recognition neural networks that are trained to identify speaker identity from a given utterance. Previous studies have widely used class activation map (CAM)-based metho...
| Authors | Yanze Xu, Mark D. Plumbley, Wenwu Wang |
|---|---|
| Primary category | eess.AS |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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