DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic Consistency (2409.12992v1)

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 LLM. 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.

• The paper introduces DiffEditor, a new model that enhances speech editing by integrating BERT-based semantic enrichment and a first-order loss for acoustic consistency.
• It refines text-based editing for out-of-domain scenarios, achieving lower MCD, higher STOI, and improved MOS scores compared to prior methods.
• Ablation studies confirm that both the semantic and acoustic components are vital for boosting intelligibility and fluid transitions.

Enhancing Speech Editing with DiffEditor: A Technical Summary

The paper "DiffEditor: Enhancing Speech Editing with Semantic Enrichment and Acoustic Consistency" addresses pivotal challenges in text-based speech editing, particularly the intelligibility and acoustic consistency of edited speech in out-of-domain (OOD) text scenarios. Yang Chen et al. present DiffEditor, a novel model designed to improve these aspects by integrating semantic enrichment through word embeddings and refining acoustic consistency using a first-order loss function.

Introduction and Background

The proliferation of digital media has accentuated the importance of efficient and high-quality speech editing. Existing methodologies for text-based speech editing significantly focus on maintaining both intelligibility and acoustic consistency. The underlying challenge is pronounced in OOD text scenarios, where traditional models suffer due to their reliance on in-domain corpora. This paper introduces DiffEditor as a solution, proposing a model architecture that addresses these pressing issues.

Methodology

Semantic Enrichment

To enhance the intelligibility of OOD text, DiffEditor integrates word embeddings from a pretrained BERT model with phoneme embeddings. This approach enriches the semantic information of phoneme embeddings by leveraging the contextual understanding provided by BERT. This enrichment process involves upsampling the BERT-generated word embeddings to match the structure of phoneme embeddings, ensuring a seamless combination of the two. By doing so, the model effectively captures semantic nuances, enhancing the clarity and correctness of pronunciation, especially for OOD text.

Acoustic Consistency

DiffEditor employs a first-order difference loss function to ensure smooth transitions and acoustic consistency. This loss focuses on minimizing abrupt changes between adjacent frames, particularly at the editing boundaries. The first-order difference $\Delta Y$ measures the rate of change between frames, and the corresponding loss function $\mathcal{L}_{FD}$ compares these changes between ground-truth and predicted acoustic features. By enforcing this loss, the model promotes natural and fluent transitions, thereby maintaining overall speech fluency.

Experimental Evaluation

The efficacy of DiffEditor is demonstrated through comprehensive experiments using both in-domain and OOD datasets. Objective metrics include Mel-Cepstral Distortion (MCD), Short-Time Objective Intelligibility (STOI), and Perceptual Evaluation of Speech Quality (PESQ). Subjective evaluations utilize Mean Opinion Score (MOS), Fluency MOS (FMOS), and Intelligibility MOS (IMOS). DiffEditor consistently outperforms existing methods such as CampNet, EditSpeech, A$^{\mathrm{3}$T, and FluentSpeech, particularly excelling in OOD scenarios.

Results

The results reveal significant improvements across all metrics:

• Objective Metrics: DiffEditor achieves lower MCD scores, higher STOI scores, and better PESQ values compared to baseline models, indicating superior quality and intelligibility.
• Subjective Metrics: DiffEditor receives higher MOS, FMOS, and IMOS scores in user studies, confirming its enhanced performance in fluency and naturalness.

Additionally, an ablation paper underscores the contributions of each component. Removing the first-order difference loss or word embeddings from the model results in noticeable declines in performance, reaffirming their importance.

Implications and Future Work

DiffEditor represents a significant advancement in speech editing by effectively handling OOD text, thereby broadening the applicability of text-based speech editing models. The integration of deep linguistic features through BERT and the novel application of a first-order difference loss function set a new standard for achieving intelligibility and acoustic consistency. Future research may explore extending this framework to other languages and refining the embeddings to capture even more nuanced linguistic features.

In conclusion, DiffEditor offers a robust solution to the prevalent challenges in speech editing, significantly improving the quality and consistency of edited speech, especially in OOD contexts. This model not only enhances current methodologies but also opens avenues for further innovations in speech processing and text-to-speech systems.