JoyVoice: Long-Context Conditioning for Anthropomorphic Multi-Speaker Conversational Synthesis
Abstract
Large speech generation models are evolving from single-speaker, short sentence synthesis to multi-speaker, long conversation geneartion. Current long-form speech generation models are predominately constrained to dyadic, turn-based interactions. To address this, we introduce JoyVoice, a novel anthropomorphic foundation model designed for flexible, boundary-free synthesis of up to eight speakers. Unlike conventional cascaded systems, JoyVoice employs a unified E2E-Transformer-DiT architecture that utilizes autoregressive hidden representations directly for diffusion inputs, enabling holistic end-to-end optimization. We further propose a MM-Tokenizer operating at a low bitrate of 12.5 Hz, which integrates multitask semantic and MMSE losses to effectively model both semantic and acoustic information. Additionally, the model incorporates robust text front-end processing via large-scale data perturbation. Experiments show that JoyVoice achieves state-of-the-art results in multilingual generation (Chinese, English, Japanese, Korean) and zero-shot voice cloning. JoyVoice achieves top-tier results on both the Seed-TTS-Eval Benchmark and multi-speaker long-form conversational voice cloning tasks, demonstrating superior audio quality and generalization. It achieves significant improvements in prosodic continuity for long-form speech, rhythm richness in multi-speaker conversations, paralinguistic naturalness, besides superior intelligibility. We encourage readers to listen to the demo at https://jea-speech.github.io/JoyVoice
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 JoyVoice, a novel anthropomorphic multi-speaker conversational synthesis model that significantly enhances the quality and flexibility of long-form speech generation. The comprehensive analysis highlights the innovative methodology, strong experimental validation, and potential for impactful applications in the field of audio synthesis.
Methodology
The paper introduces JoyVoice, a novel end-to-end Transformer-DiT architecture that innovatively integrates autoregressive hidden representations for diffusion inputs, allowing for a more cohesive synthesis of multi-speaker conversations. The use of a low-bitrate MM-Tokenizer to manage both semantic and acoustic information is particularly noteworthy, as it addresses the challenges of modeling complex conversational dynamics. The methodology is well-structured and demonstrates a clear advancement over traditional cascaded systems, although further elaboration on the training process and hyperparameter tuning would enhance understanding.
Results & evaluation
The experiments are robust, showcasing JoyVoice's performance across multiple languages and tasks, including multilingual generation and zero-shot voice cloning. The results indicate significant improvements in audio quality, prosodic continuity, and paralinguistic naturalness, which are critical for conversational synthesis. However, the paper could benefit from a more detailed comparison with baseline models and additional metrics to substantiate the claims of superiority.
Reproducibility
While the paper provides a demo URL, it lacks detailed implementation specifics, such as code availability or dataset descriptions, which are essential for reproducibility. Clearer guidelines on the training process and data preprocessing would enhance the ability of other researchers to replicate the results.
Limitations
The paper acknowledges limitations, such as potential overfitting in specific scenarios and the need for extensive computational resources for training. However, a more thorough discussion of the limitations related to the model's scalability and adaptability to diverse conversational contexts would be beneficial.
Broader impact
JoyVoice has significant potential applications in areas such as virtual assistants, gaming, and interactive storytelling, where natural and engaging multi-speaker interactions are crucial. Its advancements could lead to more immersive user experiences and broaden the accessibility of conversational AI technologies. The main contribution of this paper is the introduction of JoyVoice, a novel anthropomorphic multi-speaker conversational synthesis model that significantly enhances the quality and flexibility of long-form speech generation. The comprehensive analysis highlights the innovative methodology, strong experimental validation, and potential for impactful applications in the field of audio synthesis.
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