TidyVoice 2026 Challenge Evaluation Plan

TL;DR

The TidyVoice 2026 Challenge aims to improve cross-lingual speaker verification by providing a multilingual dataset and standardized evaluation to foster more inclusive and language-independent speaker recognition systems.

Contribution

It introduces the TidyVoiceX dataset and a standardized challenge protocol to address language mismatch in speaker verification research.

Findings

Baseline systems established for cross-lingual verification

Evaluation protocol promotes fair comparison across models

Encourages development of language-robust speaker verification methods

Abstract

The performance of speaker verification systems degrades significantly under language mismatch, a critical challenge exacerbated by the field's reliance on English-centric data. To address this, we propose the TidyVoice Challenge for cross-lingual speaker verification. The challenge leverages the TidyVoiceX dataset from the novel TidyVoice benchmark, a large-scale, multilingual corpus derived from Mozilla Common Voice, and specifically curated to isolate the effect of language switching across approximately 40 languages. Participants will be tasked with building systems robust to this mismatch, with performance primarily evaluated using the Equal Error Rate on cross-language trials. By providing standardized data, open-source baselines, and a rigorous evaluation protocol, this challenge aims to drive research towards fairer, more inclusive, and language-independent speaker recognition technologies, directly aligning with the Interspeech 2026 theme, "Speaking Together."