XAI-Grounded Explanation Generation for Speech Deepfake Detection with Training-Free Multimodal Large Language Models

• URL: http://arxiv.org/abs/2606.16137v1
• Date: Mon, 15 Jun 2026 02:55:21 GMT
• Title: XAI-Grounded Explanation Generation for Speech Deepfake Detection with Training-Free Multimodal Large Language Models
• Abstract summary: Speech deepfake detection (SDD) systems require trustworthy explanations for reliable decision-making.<n>Traditional explainable AI (XAI) produces low-level attribution signals tightly coupled with model decisions.<n>Large language model (LLM)-based explanation generation produces generic and ungrounded descriptions.
• Score: 48.03852608747844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech deepfake detection (SDD) systems require trustworthy explanations for reliable decision-making. Existing explanation ways mainly fall into two categories. Traditional explainable AI (XAI), such as gradient-based attribution, produces low-level attribution signals tightly coupled with model decisions, and harder to be understood by human than natural language explanations. Meanwhile, large language model (LLM)-based explanation generation often produces generic and ungrounded descriptions due to the lack of heuristic evidence and task-specific supervision, stemming from limited grounded explanation datasets for SDD. We therefore propose a training-free explanation framework that integrates XAI evidence with multimodal LLMs to generate grounded and specific explanations. Using the PartialSpoof dataset, we construct a grounded explanation dataset and show that methods with XAI increase inside accuracy by over 45\%, verified through human evaluation and faithfulness checks.

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