# Vo-Ve: An Explainable Voice-Vector for Speaker Identity Evaluation

arXiv: https://arxiv.org/abs/2506.19446 · HTML: https://mlsanity.com/paper/2506.19446/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [LISE : Listenable Interpretable Speaker Embeddings](https://mlsanity.com/paper/2606.21305/) (2026)

## Abstract (arXiv)

In this paper, we propose Vo-Ve, a novel voice-vector embedding that captures speaker identity. Unlike conventional speaker embeddings, Vo-Ve is explainable, as it contains the probabilities of explicit voice attribute classes. Through extensive analysis, we demonstrate that Vo-Ve not only evaluates speaker similarity competitively with conventional techniques but also provides an interpretable explanation in terms of voice attributes. We strongly believe that Vo-Ve can enhance evaluation schemes across various speech tasks due to its high-level explainability.