ATRIE: Adaptive Tuning for Robust Inference and Emotion in Persona-Driven Speech Synthesis
Abstract
High-fidelity character voice synthesis is a cornerstone of immersive multimedia applications, particularly for interacting with anime avatars and digital humans. However, existing systems struggle to maintain consistent persona traits across diverse emotional contexts. To bridge this gap, we present ATRIE, a unified framework utilizing a Persona-Prosody Dual-Track (P2-DT) architecture. Our system disentangles generation into a static Timbre Track (via Scalar Quantization) and a dynamic Prosody Track (via Hierarchical Flow-Matching), distilled from a 14B LLM teacher. This design enables robust identity preservation (Zero-Shot Speaker Verification EER: 0.04) and rich emotional expression. Evaluated on our extended AnimeTTS-Bench (50 characters), ATRIE achieves state-of-the-art performance in both generation and cross-modal retrieval (mAP: 0.75), establishing a new paradigm for persona-driven multimedia content creation.
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
ATRIE presents a novel framework for high-fidelity, character-consistent voice synthesis that bridges semantic understanding and acoustic realization. The integration of LLM-guided emotional reasoning with a lightweight adapter represents a significant advancement in TTS technology, with potential applications across multiple domains.
Methodology
The methodology presented in ATRIE is innovative, leveraging a dual-track architecture that separates static timbre from dynamic prosody, which is a significant advancement in the field of persona-driven speech synthesis. The use of a large language model (LLM) for distilling emotional reasoning into a lightweight adapter is particularly noteworthy, as it allows for real-time inference without the computational burden of the LLM during synthesis. The contrastive persona alignment mechanism is a clever approach to ensure character identity preservation while allowing for emotional variability. Overall, the proposed methods are well-structured and address critical challenges in TTS synthesis.
Results & evaluation
The experimental evaluation is robust, utilizing a newly established benchmark, AnimeTTS-Bench, which includes a diverse set of characters and strict zero-shot protocols. The paper reports state-of-the-art results across multiple metrics, including character consistency and emotional expression accuracy, demonstrating the effectiveness of ATRIE compared to existing systems. The inclusion of both qualitative and quantitative analyses strengthens the findings, providing a comprehensive view of the system's performance.
Reproducibility
The paper provides sufficient details regarding the implementation of ATRIE, including hyperparameters, training protocols, and evaluation metrics. However, the absence of a publicly available demo or project URL limits the ability for independent verification of results. The authors do mention using PyTorch and provide a clear description of the architecture, which aids in reproducibility.
Limitations
While ATRIE shows strong performance, there are limitations noted in the paper, such as the potential latency introduced by the LLM during inference and the reliance on a well-curated reference library for optimal performance. The model's performance may degrade for characters with limited voice data, and the system's effectiveness in languages other than Japanese remains untested.
Broader impact
The implications of ATRIE are significant, particularly in entertainment, accessibility, and education. By enabling consistent and emotionally expressive voice synthesis for virtual characters, the technology can enhance user engagement in various applications. However, ethical considerations regarding voice cloning and misinformation are crucial, and the authors advocate for responsible usage and detection mechanisms. ATRIE presents a novel framework for high-fidelity, character-consistent voice synthesis that bridges semantic understanding and acoustic realization. The integration of LLM-guided emotional reasoning with a lightweight adapter represents a significant advancement in TTS technology, with potential applications across multiple domains.
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