Robust TTS Training via Self-Purifying Flow Matching for the WildSpoof 2026 TTS Track

2025-12-19

AI Summary

To address label noise and environmental diversity in real-world speech scenarios within the WildSpoof 2026 Text-to-Speech (TTS) Track, this paper proposes the Self-Purifying Flow Matching (SPFM) framework. SPFM introduces, for the first time in TTS flow matching, an explicit noisy sample routing mechanism that dynamically identifies and isolates suspicious text–speech pairs while preserving their acoustic information for unconditional flow matching training. Integrated with the open-source Supertonic model, SPFM jointly optimizes conditional and unconditional flow matching losses, employs lightweight fine-tuning, and adopts dynamic sample weighting. Experiments demonstrate that SPFM achieves the lowest Word Error Rate (WER) on the WildSpoof TTS Track, while attaining second-best perceptual scores on both UTMOS and DNSMOS—substantially improving robustness to label noise and generalization across diverse acoustic conditions.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Unsupervised & Self-Supervised Learning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

This paper presents a lightweight text-to-speech (TTS) system developed for the WildSpoof Challenge TTS Track. Our approach fine-tunes the recently released open-weight TTS model, extit{Supertonic}footnote{url{https://github.com/supertone-inc/supertonic}}, with Self-Purifying Flow Matching (SPFM) to enable robust adaptation to in-the-wild speech. SPFM mitigates label noise by comparing conditional and unconditional flow matching losses on each sample, routing suspicious text--speech pairs to unconditional training while still leveraging their acoustic information. The resulting model achieves the lowest Word Error Rate (WER) among all participating teams, while ranking second in perceptual metrics such as UTMOS and DNSMOS. These findings demonstrate that efficient, open-weight architectures like Supertonic can be effectively adapted to diverse real-world speech conditions when combined with explicit noise-handling mechanisms such as SPFM.

Problem

• Robust TTS adaptation to in-the-wild speech
• Mitigate label noise in text-speech pairs
• Enhance speech synthesis for diverse real-world conditions

Innovation

• Self-Purifying Flow Matching for noise mitigation
• Fine-tuning open-weight Supertonic TTS model
• Routing suspicious pairs to unconditional training