AST: Adaptive, Seamless, and Training-Free Precise Speech Editing
Abstract
Text-based speech editing aims to modify specific segments while preserving speaker identity and acoustic context. Existing methods rely on task-specific training, which incurs high data costs and struggles with temporal fidelity in unedited regions. Meanwhile, adapting Text-to-Speech (TTS) models often faces a trade-off between editing quality and consistency. To address these issues, we propose AST, an Adaptive, Seamless, and Training-free precise speech editing framework. Leveraging a pre-trained autoregressive TTS model, AST introduces Latent Recomposition to selectively stitch preserved source segments with newly synthesized targets. Furthermore, AST extends this latent manipulation to enable precise style editing for specific speech segments. To prevent artifacts at these edit boundaries, the framework incorporates Adaptive Weak Fact Guidance (AWFG). AWFG dynamically modulates a mel-space guidance signal, enforcing structural constraints only where necessary without disrupting the generative manifold. To fill the gap of publicly accessible benchmarks, we introduce LibriSpeech-Edit, a new and larger speech editing dataset. As existing metrics poorly evaluate temporal consistency in unedited regions, we propose Word-level Dynamic Time Warping (WDTW). Extensive experiments demonstrate that AST resolves the controllability-quality trade-off without extra training. Compared to the previous most temporally consistent baseline, AST improves consistency while reducing Word Error Rate by nearly 70%. Moreover, applying AST to a foundation TTS model reduces WDTW by 27%, achieving state-of-the-art speaker preservation and temporal fidelity.
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
The paper presents AST, a novel training-free framework for precise speech editing that effectively balances quality and controllability by leveraging latent space manipulation and adaptive guidance mechanisms. This work significantly advances the field of speech editing, providing a robust alternative to traditional task-specific approaches and establishing a new benchmark for future research.
Methodology
The proposed AST framework introduces a novel approach to speech editing by leveraging latent space manipulation from pre-trained TTS models, which is a significant departure from traditional task-specific training methods. The incorporation of Adaptive Weak Fact Guidance (AWFG) to manage edit boundaries and maintain acoustic fidelity is particularly innovative. The methodology is well-structured, with clear stages for input inversion, alignment, and generation, making it easy to follow and replicate. The use of Latent Recomposition to stitch segments together while preserving speaker identity and context is a strong contribution to the field.
Results & evaluation
The experiments are extensive and well-designed, utilizing a new dataset (LibriSpeech-Edit) that addresses previous limitations in speech editing benchmarks. The paper provides a thorough comparison against established baselines, demonstrating significant improvements in key metrics such as Word Error Rate (WER) and Word-level Dynamic Time Warping (WDTW). The results indicate that AST not only matches but often surpasses the performance of models specifically trained for speech editing, showcasing its effectiveness.
Reproducibility
The paper includes detailed implementation details, including the experimental setup and evaluation metrics, which enhances reproducibility. However, the lack of publicly available code or a demo URL limits the ability for other researchers to directly replicate the results. The introduction of a new dataset is a positive step towards facilitating reproducibility in future work.
Limitations
While the AST framework shows promise, it may still face challenges in more complex editing scenarios that involve significant alterations to the speech content. The reliance on a pre-trained TTS model may also limit the adaptability of the framework to other TTS architectures. Additionally, the subjective evaluation of audio quality and naturalness could benefit from further exploration through user studies.
Broader impact
The implications of this research are substantial, particularly for applications in media production, accessibility, and content creation. By enabling precise speech editing without the need for extensive training data, AST could democratize access to high-quality speech editing tools, fostering innovation in various fields such as entertainment, education, and assistive technologies. The paper presents AST, a novel training-free framework for precise speech editing that effectively balances quality and controllability by leveraging latent space manipulation and adaptive guidance mechanisms. This work significantly advances the field of speech editing, providing a robust alternative to traditional task-specific approaches and establishing a new benchmark for future research.
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