VividVoice: A Unified Framework for Scene-Aware Visually-Driven Speech Synthesis
Abstract
We introduce and define a novel task-Scene-Aware Visually-Driven Speech Synthesis, aimed at addressing the limitations of existing speech generation models in creating immersive auditory experiences that align with the real physical world. To tackle the two core challenges of data scarcity and modality decoupling, we propose VividVoice, a unified generative framework. First, we constructed a large-scale, high-quality hybrid multimodal dataset, Vivid-210K, which, through an innovative programmatic pipeline, establishes a strong correlation between visual scenes, speaker identity, and audio for the first time. Second, we designed a core alignment module, D-MSVA, which leverages a decoupled memory bank architecture and a cross-modal hybrid supervision strategy to achieve fine-grained alignment from visual scenes to timbre and environmental acoustic features. Both subjective and objective experimental results provide strong evidence that VividVoice significantly outperforms existing baseline models in terms of audio fidelity, content clarity, and multimodal consistency. Our demo is available at https://chengyuann.github.io/VividVoice/.
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 a novel framework for scene-aware visually-driven speech synthesis, which significantly advances the field by addressing key challenges in multimodal alignment and data scarcity. The technical contributions, particularly the innovative dataset and alignment module, position this work as a meaningful advancement in audio synthesis research, although further detail on implementation and broader applicability is needed.
Methodology
The proposed methodology, VividVoice, introduces a unified generative framework that addresses the challenges of data scarcity and modality decoupling in speech synthesis. The construction of the Vivid-210K dataset is a significant contribution, as it establishes a novel correlation between visual scenes, speaker identity, and audio. The D-MSVA alignment module is innovative, utilizing a decoupled memory bank architecture and hybrid supervision strategy, which enhances the model's ability to align visual and auditory modalities effectively. However, the paper could benefit from a more detailed description of the implementation and the specific algorithms used within the D-MSVA module.
Results & evaluation
The experimental evaluation is robust, featuring both subjective and objective assessments that demonstrate the superiority of VividVoice over existing baseline models. The results indicate improvements in audio fidelity, content clarity, and multimodal consistency, which are critical metrics in speech synthesis. However, the paper lacks a comprehensive comparison with a wider range of baseline models and does not provide enough detail on the experimental setup, such as the number of participants in subjective tests or the specific metrics used for objective evaluation.
Reproducibility
The paper does not provide sufficient implementation details that would facilitate reproducibility. While it mentions the construction of the Vivid-210K dataset and the D-MSVA module, it lacks code availability or a clear description of the training process, hyperparameters, and evaluation protocols. This limits the ability of other researchers to replicate the results and build upon this work.
Limitations
One limitation is the reliance on a single dataset (Vivid-210K), which may not generalize across different contexts or speaker demographics. Additionally, the paper does not address potential biases in the dataset or the implications of using a specific set of visual scenes. The complexity of the D-MSVA module may also pose challenges for real-time applications, which are critical in practical speech synthesis scenarios.
Broader impact
The implications of Scene-Aware Visually-Driven Speech Synthesis are significant, particularly in applications such as virtual reality, gaming, and assistive technologies. By creating more immersive auditory experiences that align with visual contexts, this research can enhance user engagement and accessibility. However, ethical considerations regarding the use of such technology, particularly in terms of deepfakes or misinformation, should be addressed. The main contribution of this paper is the introduction of a novel framework for scene-aware visually-driven speech synthesis, which significantly advances the field by addressing key challenges in multimodal alignment and data scarcity. The technical contributions, particularly the innovative dataset and alignment module, position this work as a meaningful advancement in audio synthesis research, although further detail on implementation and broader applicability is needed.
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