Deep Dubbing: End-to-End Auto-Audiobook System with Text-to-Timbre and Context-Aware Instruct-TTS
Abstract
The pipeline for multi-participant audiobook production primarily consists of three stages: script analysis, character voice timbre selection, and speech synthesis. Among these, script analysis can be automated with high accuracy using NLP models, whereas character voice timbre selection still relies on manual effort. Speech synthesis uses either manual dubbing or text-to-speech (TTS). While TTS boosts efficiency, it struggles with emotional expression, intonation control, and contextual scene adaptation. To address these challenges, we propose DeepDubbing, an end-to-end automated system for multi-participant audiobook production. The system comprises two main components: a Text-to-Timbre (TTT) model and a Context-Aware Instruct-TTS (CA-Instruct-TTS) model. The TTT model generates role-specific timbre embeddings conditioned on text descriptions. The CA-Instruct-TTS model synthesizes expressive speech by analyzing contextual dialogue and incorporating fine-grained emotional instructions. This system enables the automated generation of multi-participant audiobooks with both timbre-matched character voices and emotionally expressive narration, offering a novel solution for audiobook production.
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 main contribution of this paper is the introduction of DeepDubbing, an end-to-end automated system for multi-participant audiobook production that combines innovative text-to-timbre generation and context-aware speech synthesis. This work represents a significant advancement in the field of audio synthesis, addressing critical challenges in emotional expressiveness and character voice differentiation, thereby paving the way for more immersive audiobook experiences.
Methodology
The methodology presented in the paper is robust and innovative, leveraging a dual-component architecture that includes a Text-to-Timbre (TTT) model and a Context-Aware Instruct-TTS (CA-Instruct-TTS) model. The use of conditional flow matching for timbre generation is a significant advancement, allowing for more nuanced and contextually appropriate voice synthesis. The integration of large language models (LLMs) for both timbre description generation and emotional instruction extraction showcases a sophisticated approach to automating audiobook production. However, the paper could benefit from a more detailed explanation of the training processes and hyperparameter choices for the models.
Results & evaluation
The experimental evaluation is comprehensive, utilizing a large-scale internal dataset and employing both subjective and objective metrics to assess the performance of the proposed models. The results indicate that the DeepDubbing system achieves high levels of naturalness and emotional expressiveness in synthesized speech, outperforming baseline methods. The use of a diverse set of evaluation metrics, including Character Matching Score and Mean Opinion Scores, adds credibility to the findings. However, the paper could improve by providing more comparative analysis against a wider range of existing systems.
Reproducibility
The paper mentions the release of the BookVoice-50h dataset and provides a demo URL, which enhances reproducibility. However, specific implementation details, such as the exact configurations and training procedures for the models, are not thoroughly documented, making it challenging for other researchers to replicate the results without further guidance.
Limitations
One notable limitation is the TTT model's struggle with generating child-like voices due to the lack of authentic child speech data in the training set. This limitation could hinder the system's applicability in scenarios requiring diverse character voices. Additionally, while the paper addresses the emotional expressiveness of the CA-Instruct-TTS model, it does not explore the potential biases that might arise from the training data or the implications of using LLMs in this context.
Broader impact
The proposed DeepDubbing system has significant potential applications in the audiobook industry, particularly in automating the production of multi-participant audiobooks, which could reduce costs and production times. The ability to generate emotionally expressive and contextually aware speech could enhance user engagement and experience. Furthermore, the methodologies developed could be adapted for other applications in voice synthesis, such as gaming, virtual reality, and interactive storytelling. The main contribution of this paper is the introduction of DeepDubbing, an end-to-end automated system for multi-participant audiobook production that combines innovative text-to-timbre generation and context-aware speech synthesis. This work represents a significant advancement in the field of audio synthesis, addressing critical challenges in emotional expressiveness and character voice differentiation, thereby paving the way for more immersive audiobook experiences.
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