Robust TTS Training via Self-Purifying Flow Matching for the WildSpoof 2026 TTS Track
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, \textit{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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a novel approach to robust TTS training through Self-Purifying Flow Matching, demonstrating significant improvements in performance metrics within a challenging dataset. The combination of innovative methodology and solid experimental results positions this work as a meaningful contribution to the field of machine learning and TTS systems.
Methodology
The proposed methodology, Self-Purifying Flow Matching (SPFM), is a notable advancement in handling label noise in TTS systems. By dynamically routing suspicious text-speech pairs to unconditional training, the authors effectively mitigate the impact of mislabeled data, which is a common issue in real-world datasets. The integration of SPFM with the Supertonic architecture demonstrates a thoughtful approach to improving robustness without significantly increasing computational complexity. The paper provides a clear explanation of the flow matching losses and the rationale behind the SPFM mechanism, which enhances the understanding of its operational principles.
Results & evaluation
The experimental setup is well-structured, utilizing both the TITW-easy and TITW-hard datasets to ensure a balanced training regime. The reported results, including the lowest WER and competitive perceptual metrics, validate the effectiveness of the proposed method. However, the paper could benefit from more detailed statistical analysis of the results and comparisons with baseline models to further substantiate the claims of improvement.
Reproducibility
The paper mentions the use of a publicly available checkpoint for Supertonic and provides a clear description of the training parameters and setup. However, it lacks specific details on the implementation of SPFM, such as hyperparameter choices and the exact computational resources used, which could hinder reproducibility. Including a link to the code repository is a positive aspect, but more comprehensive documentation would be beneficial.
Limitations
One limitation is the reliance on the quality of the initial Supertonic model, which may affect the overall performance if the base model has inherent flaws. Additionally, while SPFM shows promise in mitigating label noise, the effectiveness of this approach in more diverse or extreme noisy conditions remains to be tested. The paper does not discuss potential scalability issues or the impact of varying dataset sizes on the performance of the model.
Broader impact
The findings of this research have significant implications for the development of TTS systems in real-world applications, particularly in environments where data quality is variable. The approach could be applied to other domains where label noise is prevalent, potentially improving the robustness of machine learning models across various applications. This work contributes to the ongoing efforts to make TTS systems more accessible and effective in diverse settings. This paper presents a novel approach to robust TTS training through Self-Purifying Flow Matching, demonstrating significant improvements in performance metrics within a challenging dataset. The combination of innovative methodology and solid experimental results positions this work as a meaningful contribution to the field of machine learning and TTS systems.
Author of this paper? Send a correction.