# Towards Explainable Spoofed Speech Attribution and Detection:a Probabilistic Approach for Characterizing Speech Synthesizer Components

arXiv: https://arxiv.org/abs/2502.04049 · HTML: https://mlsanity.com/paper/2502.04049/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [How to Leverage Synthetic Speech for LLM-Based ASR Systems?](https://mlsanity.com/paper/2606.29031/) (2026)
- [Towards Generalized Source Tracing for Codec-Based Deepfake Speech](https://mlsanity.com/paper/2506.07294/) (2025)
- [Sparse deepfake detection promotes better disentanglement](https://mlsanity.com/paper/2510.05696/) (2025)
- [What Counts as Real? Speech Restoration and Voice Quality Conversion Pose New Challenges to Deepfake Detection](https://mlsanity.com/paper/2603.14033/) (2026)

## Abstract (arXiv)

We propose an explainable probabilistic framework for characterizing spoofed speech by decomposing it into probabilistic attribute embeddings. Unlike raw high-dimensional countermeasure embeddings, which lack interpretability, the proposed probabilistic attribute embeddings aim to detect specific speech synthesizer components, represented through high-level attributes and their corresponding values. We use these probabilistic embeddings with four classifier back-ends to address two downstream tasks: spoofing detection and spoofing attack attribution. The former is the well-known bonafide-spoof detection task, whereas the latter seeks to identify the source method (generator) of a spoofed utterance. We additionally use Shapley values, a widely used technique in machine learning, to quantify the relative contribution of each attribute value to the decision-making process in each task. Results on the ASVspoof2019 dataset demonstrate the substantial role of duration and conversion modeling in spoofing detection; and waveform generation and speaker modeling in spoofing attack attribution. In the detection task, the probabilistic attribute embeddings achieve 99.7% balanced accuracy and 0.22% equal error rate (EER), closely matching the performance of raw embeddings (99.9% balanced accuracy and 0.22% EER). Similarly, in the attribution task, our embeddings achieve 90.23% balanced accuracy and 2.07% EER, compared to 90.16% and 2.11% with raw embeddings. These results demonstrate that the proposed framework is both inherently explainable by design and capable of achieving performance comparable to raw CM embeddings.