FlexiVoice: Enabling Flexible Style Control in Zero-Shot TTS with Natural Language Instructions
Abstract
This study proposes FlexiVoice, a text-to-speech (TTS) synthesis system capable of flexible style control with zero-shot voice cloning. The speaking style is controlled by a natural-language instruction and the voice timbre is provided by a speech reference in zero-shot manner. FlexiVoice is built with an LLM core, which takes text as input, and also takes an optional natural language instruction and an optional speech reference to control style and timbre, respectively. FlexiVoice is equipped with a novel Progressive Post-Training (PPT) scheme that progressively unlocks accurate and flexible controllability. In particular, it first employs Direct Preference Optimization (DPO) to enable FlexiVoice to accurately follow both natural language instruction and speech reference simultaneously. It then uses a multi-objective Group Relative Policy Optimization (GRPO) to disentangle style instruction, reference timbre, and textual content. Finally, it adapts instruction GRPO for more advanced instruction following. Experimental results show that FlexiVoice surpasses competing baselines and demonstrates strong capability in decoupling control factors. Human evaluations further confirm its naturalness, controllability, and robustness. Audio samples are available at https://flexi-voice.github.io.
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 FlexiVoice, a novel TTS system that enables flexible style control through natural language instructions and zero-shot voice cloning, significantly advancing the state of the art in controllable speech synthesis. The comprehensive approach to methodology, rigorous experimental validation, and potential for real-world applications underscore its significance in the field of machine learning and audio processing.
Methodology
The methodology presented in this paper is robust and innovative, particularly with the introduction of the Progressive Post-Training (PPT) framework, which systematically addresses the challenges of style-timbre-content conflict in TTS systems. The use of Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) for multi-objective training is a significant advancement in the field, allowing for effective disentanglement of style and timbre while maintaining content integrity. The incorporation of a large-scale, annotated instruction-speech dataset further enhances the model's training and performance, showcasing a comprehensive approach to improving TTS systems.
Results & evaluation
The experimental evaluation is thorough, with a well-defined setup that includes both qualitative and quantitative assessments. The authors compare FlexiVoice against a range of competitive baselines, demonstrating its superior performance in terms of instruction adherence and robustness. Human evaluations confirm the model's naturalness and controllability, providing strong empirical support for the claims made. The diverse datasets used for training and evaluation, including emotion-centric tasks and complex instruction-following benchmarks, lend credibility to the findings.
Reproducibility
The paper emphasizes reproducibility by detailing the model architecture, training objectives, and evaluation protocols. The authors commit to releasing the instruction-speech dataset, model checkpoints, and code, which is essential for facilitating replication and further research. However, the complexity of the training process and the reliance on specific datasets may pose challenges for complete reproducibility without access to the exact resources used.
Limitations
While the paper presents significant advancements, it does not address potential limitations in terms of the model's performance across diverse languages and dialects beyond English and Chinese. Additionally, the reliance on large datasets for training may limit accessibility for researchers with fewer resources. The potential for misuse of advanced TTS technology, such as generating deceptive audio, is also a concern that is briefly mentioned but not deeply explored.
Broader impact
The implications of this research are substantial, as it enhances the capabilities of TTS systems, making them more adaptable for various applications, including virtual assistants, audiobooks, and gaming. The ability to control speaking style through natural language instructions opens up new avenues for user interaction and personalization. However, ethical considerations regarding the misuse of such technology must be carefully managed to prevent potential harm. The main contribution of this paper is the introduction of FlexiVoice, a novel TTS system that enables flexible style control through natural language instructions and zero-shot voice cloning, significantly advancing the state of the art in controllable speech synthesis. The comprehensive approach to methodology, rigorous experimental validation, and potential for real-world applications underscore its significance in the field of machine learning and audio processing.
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