UniVocal: Unified Speech-Singing Code-Switching Synthesis
Abstract
We propose UniVocal, a unified framework that implicitly infers vocal modes from text context to pioneer Speech-Singing Code-Switching (SCS) Synthesis - a task where transitions are autonomously driven by textual semantics, akin to seamless human language blending. Unlike single-mode generation or systems relying on switching-control tags, our proposed UniVocal implicitly infers vocal modes solely from text context. To achieve this, we employ a data-efficient two-stage curriculum learning strategy that progressively trains a competitive TTS system to acquire the desired SCS capability. Addressing data scarcity, we introduce a scalable pipeline to synthesize diverse code-switching data that is both semantically and acoustically natural, alongside a new multi-scenario benchmark, SCSBench. To address limitations of semantic tokenizers in capturing acoustic details, we also introduce refined cent token and Chain-of-Thought (CoT) generation for planning prosody before content generation, effectively enhancing empathetic speech generation and singing melody. Experimental results demonstrate that UniVocal achieves state-of-the-art performance on SCSBench while maintaining competitive performance on regular speech and singing tasks. Audio samples are available at https://project-univocal-demo.github.io/demo/. The code and dataset are released at https://github.com/FunAudioLLM/FunResearch/tree/main/UniVocal.
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 paper introduces UniVocal, a pioneering framework for Speech-Singing Code-Switching synthesis, utilizing innovative methodologies to enhance vocal synthesis capabilities. The comprehensive approach to addressing data scarcity and improving acoustic modeling positions this work as a significant advancement in the field of audio synthesis, with potential applications across various domains.
Methodology
The methodology presented in the paper is robust and innovative, introducing a unified framework for Speech-Singing Code-Switching (SCS) synthesis. The two-stage curriculum learning strategy is a significant contribution, allowing the model to progressively learn complex vocal transitions driven by text semantics. The integration of a refined cent token and Chain-of-Thought (CoT) generation enhances prosodic and melodic control, addressing limitations in existing models that rely on semantic tokenizers. The scalable data synthesis pipeline for generating diverse code-switching data is another noteworthy aspect, demonstrating a practical approach to overcoming data scarcity in this domain.
Results & evaluation
The experimental evaluation is thorough, with results demonstrating state-of-the-art performance on the newly introduced SCSBench benchmark. The paper provides a comprehensive analysis of the model's capabilities across different tasks, including empathetic speech generation and singing. The use of both objective metrics (e.g., WER, SIM, UTMOS) and subjective evaluations (e.g., human ratings for empathy and musicality) strengthens the validity of the results. The ablation studies effectively highlight the contributions of specific components, such as the refined cent token and the curriculum learning strategy.
Reproducibility
The paper includes detailed descriptions of the model architecture, training procedures, and evaluation methodologies, which support reproducibility. The authors provide links to the code and datasets, facilitating further research and validation of their findings. However, the complexity of the model and the need for substantial computational resources may pose challenges for complete replication.
Limitations
The paper acknowledges limitations related to the quality of synthetic singing data and the potential for artifacts in generated audio. Additionally, the reliance on explicit semantic triggers for robust generalization in real-world scenarios indicates that the model may struggle with purely implicit transitions. These limitations suggest areas for future improvement, particularly in enhancing the model's adaptability to diverse contexts.
Broader impact
The implications of this research extend to various applications in audio synthesis, including entertainment, education, and assistive technologies. The ability to seamlessly switch between speech and singing could enhance user experiences in interactive media, storytelling, and educational tools. However, the potential for misuse in generating deepfakes raises ethical considerations that must be addressed in future developments. The paper introduces UniVocal, a pioneering framework for Speech-Singing Code-Switching synthesis, utilizing innovative methodologies to enhance vocal synthesis capabilities. The comprehensive approach to addressing data scarcity and improving acoustic modeling positions this work as a significant advancement in the field of audio synthesis, with potential applications across various domains.
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