# OV-InstructTTS: Towards Open-Vocabulary Instruct Text-to-Speech

arXiv: https://arxiv.org/abs/2601.01459 · HTML: https://mlsanity.com/paper/2601.01459/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2026.

## Built on by (most cited first, 6 of 6)

- [Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts](https://mlsanity.com/paper/2605.28063/) (2026)
- [Unlocking Speech-Text Compositional Powers: Instruction-Following Speech Language Models without Instruction Tuning](https://mlsanity.com/paper/2607.02214/) (2026)
- [Poly-InstructTTS: Learning In-the-Wild Expressive Speech Synthesis from Open-Ended Instructions](https://mlsanity.com/paper/2608.20387/) (2026)
- [Scalable Direction-Following TTS via Voice Impression-Guided Pseudo Triplet Construction](https://mlsanity.com/paper/2609.02623/) (2026)
- [From Script to Drama: An Agentic Framework for Controllable Multi-Speaker Dialogue TTS](https://mlsanity.com/paper/2609.33362/) (2026)
- [SCIC: Scope- and Codebook-Aware Instruction Conditioning for Speaker-Adapted Expressive TTS](https://mlsanity.com/paper/2609.39088/) (2026)

## Abstract (arXiv)

Instruct Text-to-Speech (InstructTTS) leverages natural language descriptions as style prompts to guide speech synthesis. However, existing InstructTTS methods mainly rely on a direct combination of audio-related labels or their diverse rephrasings, making it difficult to handle flexible, high-level instructions. Such rigid control is insufficient for users such as content creators who wish to steer generation with descriptive instructions. To address these constraints, we introduce OV-InstructTTS, a new paradigm for open-vocabulary InstructTTS. We propose a comprehensive solution comprising a newly curated dataset, OV-Speech, and a novel reasoning-driven framework. The OV-Speech dataset pairs speech with open-vocabulary instructions, each augmented with a reasoning process that connects high-level instructions to acoustic features. The reasoning-driven framework infers emotional, acoustic, and paralinguistic information from open-vocabulary instructions before synthesizing speech. Evaluations show that this reasoning-driven approach significantly improves instruction-following fidelity and speech expressiveness. We believe this work can inspire the next user-friendly InstructTTS systems with stronger generalization and real-world applicability. The dataset and demos are publicly available on our project page.