Instance-Specific Test-Time Training for Speech Editing in the Wild

Abstract

Speech editing systems aim to naturally modify speech content while preserving acoustic consistency and speaker identity. However, previous studies often struggle to adapt to unseen and diverse acoustic conditions, resulting in degraded editing performance in real-world scenarios. To address this, we propose an instance-specific test-time training method for speech editing in the wild. Our approach employs direct supervision from ground-truth acoustic features in unedited regions and indirect supervision in edited regions via auxiliary losses based on duration constraints and phoneme prediction. This strategy mitigates the bandwidth discontinuity problem in speech editing, ensuring smooth acoustic transitions between unedited and edited regions. Additionally, it enables precise control over speech rate by adapting the model to target durations via mask length adjustment during test-time training. Experiments on in-the-wild benchmark datasets demonstrate that our method outperforms existing speech editing systems in both objective and subjective evaluations.