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DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic Consistency

Yang Chen, Yuhang Jia, Shiwan Zhao, Ziyue Jiang, Haoran Li, Jiarong Kang, Yong Qin

TL;DR

DiffEditor is introduced, a novel speech editing model designed to enhance performance in OOD text scenarios through semantic enrichment and acoustic consistency, and proposes a first-order loss function to promote smoother transitions at editing boundaries and enhance the overall fluency of the edited speech.

Abstract

As text-based speech editing becomes increasingly prevalent, the demand for unrestricted free-text editing continues to grow. However, existing speech editing techniques encounter significant challenges, particularly in maintaining intelligibility and acoustic consistency when dealing with out-of-domain (OOD) text. In this paper, we introduce, DiffEditor, a novel speech editing model designed to enhance performance in OOD text scenarios through semantic enrichment and acoustic consistency. To improve the intelligibility of the edited speech, we enrich the semantic information of phoneme embeddings by integrating word embeddings extracted from a pretrained language model. Furthermore, we emphasize that interframe smoothing properties are critical for modeling acoustic consistency, and thus we propose a first-order loss function to promote smoother transitions at editing boundaries and enhance the overall fluency of the edited speech. Experimental results demonstrate that our model achieves state-of-the-art performance in both in-domain and OOD text scenarios.

DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic Consistency

TL;DR

DiffEditor is introduced, a novel speech editing model designed to enhance performance in OOD text scenarios through semantic enrichment and acoustic consistency, and proposes a first-order loss function to promote smoother transitions at editing boundaries and enhance the overall fluency of the edited speech.

Abstract

As text-based speech editing becomes increasingly prevalent, the demand for unrestricted free-text editing continues to grow. However, existing speech editing techniques encounter significant challenges, particularly in maintaining intelligibility and acoustic consistency when dealing with out-of-domain (OOD) text. In this paper, we introduce, DiffEditor, a novel speech editing model designed to enhance performance in OOD text scenarios through semantic enrichment and acoustic consistency. To improve the intelligibility of the edited speech, we enrich the semantic information of phoneme embeddings by integrating word embeddings extracted from a pretrained language model. Furthermore, we emphasize that interframe smoothing properties are critical for modeling acoustic consistency, and thus we propose a first-order loss function to promote smoother transitions at editing boundaries and enhance the overall fluency of the edited speech. Experimental results demonstrate that our model achieves state-of-the-art performance in both in-domain and OOD text scenarios.
Paper Structure (14 sections, 2 equations, 2 figures, 3 tables)

This paper contains 14 sections, 2 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. METHODOLOGY
  3. Overall Workflow
  4. Semantic Enrichment
  5. Acoustic Consistency
  6. EXPERIMENTS AND RESULTS
  7. Dataset
  8. Experimental Setup
  9. Evaluation Metrics
  10. Comparative Study
  11. Main Results
  12. Ablation Study
  13. Editing Boundaries Analysis
  14. CONCLUSION

Figures (2)

  • Figure 1: Overview of the DiffEditor workflow. 1) The Condition C section illustrates the complete generation process for condition C, with the inner Semantic Enrichment section representing the Semantic Enrichment method. 2) The right section depicts the Diffusion Model, while the First-Order Difference Loss facilitates Acoustic Consistency.
  • Figure 2: Visualizations of the generated mel-spectrogram boundary by DiffEditor and FluentSpeech