Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement
Abstract
This paper presents a new challenge that calls for zero-shot text-to-speech (TTS) systems to augment speech data for the downstream task, personalized speech enhancement (PSE), as part of the Generative Data Augmentation workshop at ICASSP 2025. Collecting high-quality personalized data is challenging due to privacy concerns and technical difficulties in recording audio from the test scene. To address these issues, synthetic data generation using generative models has gained significant attention. In this challenge, participants are tasked first with building zero-shot TTS systems to augment personalized data. Subsequently, PSE systems are asked to be trained with this augmented personalized dataset. Through this challenge, we aim to investigate how the quality of augmented data generated by zero-shot TTS models affects PSE model performance. We also provide baseline experiments using open-source zero-shot TTS models to encourage participation and benchmark advancements. Our baseline code implementation and checkpoints are available online.
Cite
@article{arxiv.2501.13372,
title = {Generative Data Augmentation Challenge: Zero-Shot Speech Synthesis for Personalized Speech Enhancement},
author = {Jae-Sung Bae and Anastasia Kuznetsova and Dinesh Manocha and John Hershey and Trausti Kristjansson and Minje Kim},
journal= {arXiv preprint arXiv:2501.13372},
year = {2025}
}
Comments
Accepted to ICASSP 2025 Satellite Workshop: Generative Data Augmentation for Real-World Signal Processing Applications