WordVoice: Explicit and Decoupled Multi-Dimensional Word-Level Control for LLM-Based TTS
Score
75首次提出LLM-TTS中显式解耦词级多维控制方法，填补领域空白，数据与架构均有创新。
方法描述清晰，数据集构建严谨，实验对比充分，但缺乏与基线在真实场景中的详细案例分析。
直接适用于音频书、配音等需精确控制的生产场景，代码与样本公开便于落地。
发布于2025年7月（arXiv:2607.06461），紧跟LLM-TTS前沿，但距投稿日期较近。
来自arXiv预印本，尚未经同行评审，但作者公开代码与样本，可信度中等偏上。
Base 83.5 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
High novelty, first to address word-level multi-dimensional control in LLM-TTS with a complete dataset and architecture design, supported by thorough experiments.
AI commentary
「This paper is noteworthy because it directly addresses a critical pain point for practical LLM-TTS: fine-grained control. For developers and researchers working on speech synthesis, audiobook production, and video dubbing, WordVoice's decoupled word-level control enables precise adjustment of speed, stress, and emotion without sacrificing naturalness. The editor believes this method has strong potential for industrial adoption, especially combined with LLM's language understanding capabilities, promising more natural prosodic adaptation in the future.」
Quick overview
Recent LLM-based TTS systems achieve remarkable naturalness but rely on implicit end-to-end generation, resulting in coarse-grained control. In scenarios demanding precise stylistic interventions and strict temporal alignment, such as audiobook narration and video dubbing, the inability to explicitly manipulate word-level acoustic attributes remains a critical bottleneck. This paper proposes WordVoice, a unified framework: first, it constructs WordVoice-5A, a 4.7k-hour bilingual dataset with five-dimensional word-level annotations (duration, boundary, energy, pitch, and tone) developed through a linguistically-guided pipeline; second, it introduces a bound-token mechanism to transform implicit generation into an explicit controllable paradigm, enabling adaptive multi-task prosodic planning and flexible manual intervention; third, it augments the token-to-waveform stage with a fine-grained acoustic modulation module to bridge the resolution gap between highly compressed discrete tokens and continuous waveforms. Experiments show WordVoice achieves superior decoupled control over multiple acoustic dimensions while maintaining competitive zero-shot synthesis stability. Code and audio samples are publicly available.
- Constructs WordVoice-5A: a 4.7k-hour bilingual dataset with five-dimensional word-level acoustic annotations.
- Proposes a bound-token mechanism for explicit acoustic planning, enabling multi-task prosody control and manual intervention.
- Introduces a fine-grained acoustic modulation module to align word-level attributes between discrete tokens and continuous waveforms.
- Experiments demonstrate superior decoupled control across multiple acoustic dimensions while maintaining competitive zero-shot synthesis stability.
Full text
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