arXiv:2607.06461v1 Announce Type: cross Abstract: While recent Large Language Model (LLM)-based Text-to-Speech (TTS) systems have achieved remarkable naturalness, they predominantly rely on implicit end-to-end generation paradigms, 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 limitation is primarily amplified by the severe scarcity of fine-grained annotated datasets
Advances in LLM technology are pushing the boundaries of TTS capabilities, making explicit word-level control the next frontier for practical applications.
This development addresses a critical bottleneck in LLM-based TTS, enabling high-fidelity, fine-grained control essential for professional content creation like audiobooks and dubbing.
The ability to precisely manipulate word-level acoustic attributes will transform speech synthesis from coarse-grained to highly customizable, opening new creative and commercial avenues.
- · Media and entertainment companies
- · Audiobook publishers
- · AI voice actors
- · Content creators
- · Generic end-to-end TTS providers
- · Voice-over artists with limited technical skills
Wider adoption of AI-generated voices for professional and creative content.
Increased demand for specialized tooling and platforms that leverage fine-grained TTS control.
Potential for an explosion in personalized, dynamic audio experiences across various digital mediums.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL