# Explainable Attribute-Based Speaker Verification

arXiv: https://arxiv.org/abs/2405.19796 · HTML: https://mlsanity.com/paper/2405.19796/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 30 May 2024.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Concept Bottleneck Models](https://mlsanity.com/paper/2007.04612/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Grad-CAM: Why did you say that?](https://mlsanity.com/paper/1611.07450/) (2016)
- [DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems](https://mlsanity.com/paper/1906.10263/) (2019)
- [On Specifying for Trustworthiness](https://mlsanity.com/paper/2206.11421/) (2022)
- [Explanations for Automatic Speech Recognition](https://mlsanity.com/paper/2302.14062/) (2023)
- [Leveraging speaker attribute information using multi task learning for speaker verification and diarization](https://mlsanity.com/paper/2010.14269/) (2020)

## Built on by (most cited first, 11 of 11)

- [CoLMbo: Speaker Language Model for Descriptive Profiling](https://mlsanity.com/paper/2506.09375/) (2025)
- [The Voice Timbre Attribute Detection 2025 Challenge Evaluation Plan](https://mlsanity.com/paper/2505.09382/) (2025)
- [The Impact of Automatic Speech Transcription on Speaker Attribution](https://mlsanity.com/paper/2507.08660/) (2025)
- [SpeakerLLM: A Speaker-Specialized Audio-LLM for Speaker Understanding and Verification Reasoning](https://mlsanity.com/paper/2605.15044/) (2026)
- [Sparse Autoencoder Insights on Voice Embeddings](https://mlsanity.com/paper/2502.00127/) (2025)
- [Introducing voice timbre attribute detection](https://mlsanity.com/paper/2505.09661/) (2025)
- [The First Voice Timbre Attribute Detection Challenge](https://mlsanity.com/paper/2509.06635/) (2025)
- [LISE : Listenable Interpretable Speaker Embeddings](https://mlsanity.com/paper/2606.21305/) (2026)
- [Towards Dys-XAI: Influence-Based Explanations for Dysarthria Severity Assessment](https://mlsanity.com/paper/2606.21306/) (2026)
- [Disentangling Representation using Attributes-based Gaussian Estimation for Medical Sound Diagnosis](https://mlsanity.com/paper/2608.29026/) (2026)
- [Exploring Second-Order Pattern Recognition in Speaker Recognition](https://mlsanity.com/paper/2609.11182/) (2026)

## Abstract (arXiv)

This paper proposes a fully explainable approach to speaker verification (SV), a task that fundamentally relies on individual speaker characteristics. The opaque use of speaker attributes in current SV systems raises concerns of trust. Addressing this, we propose an attribute-based explainable SV system that identifies speakers by comparing personal attributes such as gender, nationality, and age extracted automatically from voice recordings. We believe this approach better aligns with human reasoning, making it more understandable than traditional methods. Evaluated on the Voxceleb1 test set, the best performance of our system is comparable with the ground truth established when using all correct attributes, proving its efficacy. Whilst our approach sacrifices some performance compared to non-explainable methods, we believe that it moves us closer to the goal of transparent, interpretable AI and lays the groundwork for future enhancements through attribute expansion.