SeamlessEdit: Background Noise Aware Zero-Shot Speech Editing with in-Context Enhancement
For researchers and practitioners in speech editing, this work addresses the real-world problem of background noise, which was previously ignored, but the improvement is incremental over existing methods.
SeamlessEdit tackles noisy speech editing by introducing a noise-resilient framework with frequency-band-aware noise suppression and in-context refinement, outperforming state-of-the-art methods in quantitative and qualitative evaluations.
With the fast development of zero-shot text-to-speech technologies, it is possible to generate high-quality speech signals that are indistinguishable from the real ones. Speech editing, including speech insertion and replacement, appeals to researchers due to its potential applications. However, existing studies only considered clean speech scenarios. In real-world applications, the existence of environmental noise could significantly degrade the quality of generation. In this study, we propose a noise-resilient speech editing framework, SeamlessEdit, for noisy speech editing. SeamlessEdit adopts a frequency-band-aware noise suppression module and an in-content refinement strategy. It can well address the scenario where the frequency bands of voice and background noise are not separated. The proposed SeamlessEdit framework outperforms state-of-the-art approaches in multiple quantitative and qualitative evaluations.