Researchers have introduced WordVoice, a novel framework designed to enhance control over Large Language Model (LLM)-based Text-to-Speech (TTS) systems. This system addresses the limitations of current implicit generation methods by enabling explicit, multi-dimensional word-level acoustic manipulation. To support this, a substantial bilingual dataset named WordVoice-5A, featuring five dimensions of word-level annotations, was created. The WordVoice framework incorporates a bound-token mechanism for acoustic planning and a fine-grained modulation module to bridge the gap between discrete tokens and continuous waveforms, offering superior control while maintaining synthesis stability. AI
IMPACT Enhances control over LLM-based TTS systems, potentially improving applications like audiobook narration and video dubbing.
RANK_REASON The cluster contains a research paper detailing a new framework and dataset for TTS.
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