Multi Codec Discrete Diffusion Model for Text Guided Speech Inpainting and Editing
SIEDD model achieves best speech-editing performance on RealEdit benchmark, surpassing autoregressive baselines.
Key Findings
Methodology
SIEDD employs a discrete diffusion framework for text-guided speech inpainting and editing. Its core architecture, HiCoDD, follows the RVQ generation order by treating previously generated codebooks as clean acoustic context and applying diffusion only to the current refinement codebook. The model combines phoneme-level conditioning, span-localized classifier-free guidance, and duration prediction to support both fixed-duration inpainting and variable-duration text edits.
Key Results
- On the RealEdit benchmark, SIEDD achieves the lowest WER and MCD, and the highest speaker similarity among evaluated methods.
- SIEDD outperforms evaluated autoregressive baselines across all speech-inpainting settings, on both single and multiple gaps.
- Experiments demonstrate that explicitly modeling the codec hierarchy substantially improves context-preserving speech reconstruction and editing.
Significance
SIEDD is significant in the field of speech inpainting and editing. It not only excels on the RealEdit benchmark but also demonstrates superiority in various speech inpainting settings. By explicitly modeling the codec hierarchy, the model achieves higher-quality speech reconstruction while preserving context.
Technical Contribution
SIEDD's technical contribution lies in its discrete diffusion framework, which achieves context-preserving speech reconstruction and editing by explicitly modeling the codec hierarchy. It outperforms traditional autoregressive methods in multiple speech inpainting settings.
Novelty
SIEDD is the first to apply discrete diffusion to text-guided speech inpainting and editing, explicitly modeling the codec hierarchy. This innovation allows the model to achieve high-quality speech reconstruction while preserving context.
Limitations
- The model's performance may degrade for long-duration gaps.
- Adaptability to diverse speech datasets needs verification.
Future Work
Future research can explore SIEDD's application in multilingual environments and its performance in more complex speech editing tasks.
AI Executive Summary
Speech recordings often contain missing, corrupted, or incorrect regions that must be reconstructed or modified without re-synthesizing the entire utterance. The SIEDD model achieves text-guided speech inpainting and editing through a discrete diffusion framework, with its core architecture HiCoDD following the RVQ generation order by treating previously generated codebooks as clean acoustic context and applying diffusion only to the current refinement codebook. The model achieves the best speech-editing performance on the RealEdit benchmark, surpassing evaluated autoregressive baselines. By explicitly modeling the codec hierarchy, SIEDD significantly improves context-preserving speech reconstruction and editing. Although its performance may degrade for long-duration gaps, SIEDD excels in various speech inpainting settings. Future research can explore its application in multilingual environments and its performance in more complex speech editing tasks.
Deep Analysis
Background
Speech inpainting and editing are crucial research areas in speech processing. Traditional methods rely on signal-processing assumptions like local stationarity and autoregressive prediction but often fail when dealing with missing regions spanning phonemes, syllables, or words. Recent work has shifted towards generative modeling approaches like diffusion, flow matching, and neural codec language models.
Core Problem
Speech inpainting and editing require the generated speech to express the intended words while remaining consistent with the surrounding speaker identity, prosody, timing, and recording conditions. Traditional methods often fail when dealing with long-duration missing regions, necessitating new approaches to address this challenge.
Innovation
SIEDD achieves text-guided speech inpainting and editing through a discrete diffusion framework. Its core innovation lies in explicitly modeling the codec hierarchy, allowing the model to achieve high-quality speech reconstruction while preserving context.
Methodology
- �� SIEDD employs a discrete diffusion framework for text-guided speech inpainting and editing. • HiCoDD follows the RVQ generation order by treating previously generated codebooks as clean acoustic context. • The model combines phoneme-level conditioning, span-localized classifier-free guidance, and duration prediction.
Experiments
Experiments were conducted on the RealEdit benchmark, evaluating SIEDD's performance across various speech inpainting settings. Evaluation metrics included WER, MCD, and speaker similarity.
Results
SIEDD achieves the best speech-editing performance on the RealEdit benchmark, surpassing evaluated autoregressive baselines. Experiments demonstrate that explicitly modeling the codec hierarchy substantially improves context-preserving speech reconstruction and editing.
Applications
SIEDD can be used to repair damaged recordings, remove localized noise or artifacts, recover packet losses, and restore omitted speech.
Limitations & Outlook
Although SIEDD excels in various speech inpainting settings, its performance may degrade for long-duration gaps. Future research can explore its application in multilingual environments.
Plain Language Accessible to non-experts
Imagine a jigsaw puzzle game where some pieces are missing. SIEDD acts like a smart puzzle master, perfectly filling in the missing pieces based on the surrounding ones. It not only repairs missing parts but also modifies certain sections according to your instructions, making the puzzle look even more perfect. Just like in a concert where an instrument suddenly goes silent, SIEDD acts like a versatile musician, seamlessly filling in the missing notes, allowing the music to continue flowing smoothly.
ELI14 Explained like you're 14
Imagine you're playing a super complex jigsaw puzzle game, and some pieces are missing! SIEDD is like a super smart puzzle master that can perfectly fill in those missing pieces based on the surrounding ones. It not only repairs missing parts but also modifies certain sections according to your instructions, making the puzzle look even more perfect. Just like in a concert where an instrument suddenly goes silent, SIEDD is like a versatile musician that can seamlessly fill in the missing notes, allowing the music to continue flowing smoothly. Isn't that cool?
Glossary
Discrete Diffusion
A generative model that reconstructs data by iteratively denoising it.
Used for generating and repairing speech segments.
RVQ (Residual Vector Quantization)
An encoding technique that refines audio signals layer by layer.
Used to represent the hierarchical structure of speech.
Phoneme-level Conditioning
Constraining the generation process at the phoneme level.
Used to improve the accuracy of speech generation.
Classifier-free Guidance
A generation technique that improves quality by combining conditional and unconditional predictions.
Used to enhance the accuracy of speech editing.
RealEdit Benchmark
A benchmark dataset for evaluating speech editing performance.
Used to test SIEDD's performance.
Open Questions Unanswered questions from this research
- 1 How can SIEDD be applied in multilingual environments?
- 2 How can SIEDD's performance for long-duration gaps be improved?
Applications
Immediate Applications
Recording Repair
SIEDD can be used to repair damaged recordings, remove localized noise or artifacts.
Long-term Vision
Multilingual Speech Editing
Explore SIEDD's application in multilingual environments to enhance cross-language speech editing performance.
Abstract
Speech recordings often contain missing, corrupted, or incorrect regions that must be reconstructed or modified without re-synthesizing the entire utterance. Speech inpainting restores missing segments, whereas speech editing replaces spoken content according to an edited transcript. Both tasks require the generated speech to express the intended words while remaining consistent with the surrounding speaker identity, prosody, timing, and recording conditions. Discrete diffusion is particularly well suited to these tasks because it can iteratively refine masked tokens while jointly conditioning on both left and right acoustic context. We introduce SIEDD, a discrete diffusion framework for text-guided speech inpainting and editing over hierarchical codec tokens. Its core architecture, HiCoDD, follows the RVQ generation order by representing previously generated codebooks as clean, committed acoustic context and applying diffusion only to the current refinement codebook. This separation enables leakage-free joint training while matching sequential coarse-to-fine inference. The model further combines phoneme-level conditioning, span-localized classifier-free guidance, and duration prediction to support both fixed-duration inpainting and variable-duration text edits. On the RealEdit benchmark, SIEDD achieves the best overall speech-editing performance among the evaluated methods. It also outperforms the evaluated autoregressive baselines across all speech-inpainting settings, on both single and multiple gaps. These results demonstrate that explicitly modeling the codec hierarchy substantially improves context-preserving speech reconstruction and editing. See our full code at https://github.com/iftachShoham/SIEDD.