Edit Content, Preserve Acoustics: Imperceptible Text-Based Speech Editing via Self-Consistency Rewards
Abstract
Imperceptible text-based speech editing allows users to modify spoken content by altering the transcript. It demands that modified segments fuse seamlessly with the surrounding context. Prevalent methods operating in the acoustic space suffer from inherent content-style entanglement, leading to generation instability and boundary artifacts. In this paper, we propose a novel framework grounded in the principle of "Edit Content, Preserve Acoustics". Our approach relies on two core components: (1) Structural Foundations, which decouples editing into a stable semantic space while delegating acoustic reconstruction to a Flow Matching decoder; and (2) Perceptual Alignment, which employs a novel Self-Consistency Rewards Group Relative Policy Optimization. By leveraging a pre-trained Text-to-Speech model as an implicit critic -- complemented by strict intelligibility and duration constraints -- we effectively align the edited semantic token sequence with the original context. Empirical evaluations demonstrate that our method significantly outperforms state-of-the-art autoregressive and non-autoregressive baselines, achieving superior intelligibility, robustness, and perceptual quality.
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 a novel framework for imperceptible text-based speech editing that effectively separates content modification from acoustic reconstruction. This approach significantly advances the state of the art, addressing key challenges in speech editing and offering promising applications across multiple domains.
Methodology
The proposed methodology introduces a novel framework for text-based speech editing that effectively decouples semantic content from acoustic features, addressing the limitations of existing methods that often lead to artifacts and instability. The use of a Flow Matching decoder for acoustic reconstruction and a Self-Consistency Rewards mechanism for perceptual alignment is innovative and well-justified, leveraging a pre-trained TTS model as an implicit critic. This dual-stage approach enhances both intelligibility and naturalness, making significant strides in the field of speech editing.
Results & evaluation
The experiments are comprehensive, utilizing a large-scale dataset (Libriheavy) and rigorous benchmarks for evaluation. The authors provide detailed comparisons against state-of-the-art models, demonstrating clear improvements in metrics such as WER, speaker similarity, and perceptual quality. The use of both objective and subjective metrics strengthens the evaluation, although further details on the statistical significance of results would enhance the robustness of the findings.
Reproducibility
The paper includes sufficient implementation details, including training configurations and the architecture of the models used. However, the absence of a publicly available code repository limits full reproducibility. Providing access to the code and trained models would significantly enhance the paper's impact and allow for independent verification of results.
Limitations
While the proposed method shows strong performance, the paper does not address potential limitations in terms of computational efficiency or the scalability of the approach to diverse languages or dialects. Additionally, the reliance on a pre-trained TTS model may introduce biases based on the training data used for that model.
Broader impact
The implications of this research are significant for various applications, including media production, accessibility technologies, and real-time speech editing in communication tools. The ability to edit speech seamlessly could enhance user experience and efficiency in numerous fields, from entertainment to education. The paper presents a novel framework for imperceptible text-based speech editing that effectively separates content modification from acoustic reconstruction. This approach significantly advances the state of the art, addressing key challenges in speech editing and offering promising applications across multiple domains.
Author of this paper? Send a correction.