# FluentEditor2: Text-based Speech Editing by Modeling Multi-Scale Acoustic and Prosody Consistency

arXiv: https://arxiv.org/abs/2410.03719 · HTML: https://mlsanity.com/paper/2410.03719/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 80 references that are in the map.
- First posted on arXiv 28 Sep 2024.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Keystroke Dynamics as Part of Lifelogging](https://mlsanity.com/paper/2010.10144/) (2020)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [Diff-TTS: A Denoising Diffusion Model for Text-to-Speech](https://mlsanity.com/paper/2104.01409/) (2021)
- [Recent Developments on ESPnet Toolkit Boosted by Conformer](https://mlsanity.com/paper/2010.13956/) (2020)
- [Data Augmenting Contrastive Learning of Speech Representations in the Time Domain](https://mlsanity.com/paper/2007.00991/) (2020)
- [PortaSpeech: Portable and High-Quality Generative Text-to-Speech](https://mlsanity.com/paper/2109.15166/) (2021)
- [A3T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing](https://mlsanity.com/paper/2203.09690/) (2022)
- [EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion](https://mlsanity.com/paper/2107.01554/) (2021)
- [Expressive TTS Training with Frame and Style Reconstruction Loss](https://mlsanity.com/paper/2008.01490/) (2020)
- [MsEmoTTS: Multi-scale emotion transfer, prediction, and control for emotional speech synthesis](https://mlsanity.com/paper/2201.06460/) (2022)
- [EdiTTS: Score-based Editing for Controllable Text-to-Speech](https://mlsanity.com/paper/2110.02584/) (2021)
- [Adversarial Contrastive Estimation](https://mlsanity.com/paper/1805.03642/) (2018)
- [FluentSpeech: Stutter-Oriented Automatic Speech Editing with Context-Aware Diffusion Models](https://mlsanity.com/paper/2305.13612/) (2023)
- [SpeechFormer: A Hierarchical Efficient Framework Incorporating the Characteristics of Speech](https://mlsanity.com/paper/2203.03812/) (2022)
- [Towards Multi-Scale Style Control for Expressive Speech Synthesis](https://mlsanity.com/paper/2104.03521/) (2021)
- [CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing](https://mlsanity.com/paper/2202.09950/) (2022)
- [Self-supervised Context-aware Style Representation for Expressive Speech Synthesis](https://mlsanity.com/paper/2206.12559/) (2022)
- [DCTTS: Discrete Diffusion Model with Contrastive Learning for Text-to-speech Generation](https://mlsanity.com/paper/2309.06787/) (2023)
- [FluentEditor: Text-based Speech Editing by Considering Acoustic and Prosody Consistency](https://mlsanity.com/paper/2309.11725/) (2023)
- [MSStyleTTS: Multi-Scale Style Modeling with Hierarchical Context Information for Expressive Speech Synthesis](https://mlsanity.com/paper/2307.16012/) (2023)
- [Towards Expressive Zero-Shot Speech Synthesis with Hierarchical Prosody Modeling](https://mlsanity.com/paper/2406.05681/) (2024)
- [DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic Consistency](https://mlsanity.com/paper/2409.12992/) (2024)

## Built on by (most cited first, 1 of 1)

- [dots.tts.edit: Precisely Controlled Speech Editing with a Continuous Autoregressive Model](https://mlsanity.com/paper/2608.02673/) (2026)

## Abstract (arXiv)

Text-based speech editing (TSE) allows users to edit speech by modifying the corresponding text directly without altering the original recording. Current TSE techniques often focus on minimizing discrepancies between generated speech and reference within edited regions during training to achieve fluent TSE performance. However, the generated speech in the edited region should maintain acoustic and prosodic consistency with the unedited region and the original speech at both the local and global levels. To maintain speech fluency, we propose a new fluency speech editing scheme based on our previous textitFluentEditor model, termed textitFluentEditor2, by modeling the multi-scale acoustic and prosody consistency training criterion in TSE training. Specifically, for local acoustic consistency, we propose textithierarchical local acoustic smoothness constraint to align the acoustic properties of speech frames, phonemes, and words at the boundary between the generated speech in the edited region and the speech in the unedited region. For global prosody consistency, we propose textitcontrastive global prosody consistency constraint to keep the speech in the edited region consistent with the prosody of the original utterance. Extensive experiments on the VCTK and LibriTTS datasets show that textitFluentEditor2 surpasses existing neural networks-based TSE methods, including Editspeech, Campnet, A3T, FluentSpeech, and our Fluenteditor, in both subjective and objective. Ablation studies further highlight the contributions of each module to the overall effectiveness of the system. Speech demos are available at: urlhttps://github.com/Ai-S2-Lab/FluentEditor2.