# EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering

arXiv: https://arxiv.org/abs/2508.03543 · HTML: https://mlsanity.com/paper/2508.03543/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Activation steering methods, one of 13 directions in Mechanistic interpretability.
- First posted on arXiv 5 Aug 2025.

## Placed in

- Mechanistic interpretability / [Activation steering methods](https://mlsanity.com/mechinterp/activation-steering-methods/)

## Built on by (most cited first, 23 of 23)

- [CoCoEmo: Composable and Controllable Human-Like Emotional TTS via Activation Steering](https://mlsanity.com/paper/2602.03420/) (2026)
- [EmoShift: Lightweight Activation Steering for Enhanced Emotion-Aware Speech Synthesis](https://mlsanity.com/paper/2601.22873/) (2026)
- [Sparse Autoencoders for Interpretable Emotion Control in Text-to-Speech](https://mlsanity.com/paper/2606.01479/) (2026)
- [A Geometric Perspective on Composable Emotion Steering in Text-to-Speech Models](https://mlsanity.com/paper/2607.00946/) (2026)
- [Neuron-Level Emotion Control in Speech-Generative Large Audio-Language Models](https://mlsanity.com/paper/2603.17231/) (2026)
- [Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models](https://mlsanity.com/paper/2605.27376/) (2026)
- [Interpreting and Steering a Text-to-Speech Language Model with Sparse Autoencoders](https://mlsanity.com/paper/2606.10029/) (2026)
- [The Sound of Absence: Audio-Language Embedding Models Struggle with Negation](https://mlsanity.com/paper/2607.12290/) (2026)
- [Multilingual Emotion Neurons in Large Audio-Language Models](https://mlsanity.com/paper/2608.08772/) (2026)
- [Linear Script Representations in Speech Foundation Models Enable Zero-Shot Transliteration](https://mlsanity.com/paper/2601.02906/) (2026)
- [DUET: Unified Dual-Space Emotion Control for Diffusion and Flow-Matching Driven Text-to-Speech](https://mlsanity.com/paper/2606.00066/) (2026)
- [Emotion Across Speech and Faces: Shared Affective Mechanisms in Multimodal Foundation Models](https://mlsanity.com/paper/2608.17102/) (2026)
- [Forget who you Forgot: Speaker Unlearning to Prevent Re-Identification in Zero-Shot Text-to-Speech](https://mlsanity.com/paper/2609.27399/) (2026)
- [ReaFlow-TTS: Realization-Conditioned Flow Matching for High-Quality and Controllable Speech Synthesis](https://mlsanity.com/paper/2609.28906/) (2026)
- [Controlling Speaking Rate in Autoregressive TTS via Activation Steering](https://mlsanity.com/paper/2609.33810/) (2026)
- [SEmoEdit: Probing and Harnessing the Editability of Pre-trained Speech Flows](https://mlsanity.com/paper/2609.34648/) (2026)
- [EmoRES-TTS: Residual-Enhanced Vector Steering for Emotional Speech Generation](https://mlsanity.com/paper/2609.38157/) (2026)
- [ParaGeo: Decomposing Paralinguistic Variation into a Shared Latent Geometry](https://mlsanity.com/paper/2610.03125/) (2026)
- [Steering Speech-Language Models: Training-Free Task Specialization via Contrastive Activation Addition](https://mlsanity.com/paper/2610.04683/) (2026)
- [Tracing a Sparse Emotion-Control Circuit in LLM-Based Text-to-Speech](https://mlsanity.com/paper/2610.05080/) (2026)
- [Loud and Clear: Dynamic Activation Steering for Improving Speech Intelligibility in Noisy Environments](https://mlsanity.com/paper/2610.07647/) (2026)
- [Steering Follows Geometry, Not Labels: Emotion Directions in a Full-Duplex Speech Model](https://mlsanity.com/paper/2610.08887/) (2026)
- [Steerspeech: Activation Steering For Emotion Control In Generated Speech](https://mlsanity.com/paper/2610.10415/) (2026)

## Abstract (arXiv)

Text-to-speech (TTS) has shown great progress in recent years. However, most existing TTS systems offer only coarse and rigid emotion control, typically via discrete emotion labels or a carefully crafted and detailed emotional text prompt, making fine-grained emotion manipulation either inaccessible or unstable. These models also require extensive, high-quality datasets for training. To address these limitations, we propose EmoSteer-TTS, a novel training-free approach, to achieve fine-grained speech emotion control (conversion, interpolation, erasure) by activation steering. We first empirically observe that modifying a subset of the internal activations within a flow matching-based TTS model can effectively alter the emotional tone of synthesized speech. Building on this insight, we then develop a training-free and efficient algorithm, including activation extraction, emotional token searching, and inference-time steering, which can be seamlessly integrated into a wide range of pretrained models (e.g., F5-TTS, CosyVoice2, and E2-TTS). In addition, to derive effective steering vectors, we construct a curated emotional speech dataset with diverse speakers. Extensive experiments demonstrate that EmoSteer-TTS enables fine-grained, interpretable, and continuous control over speech emotion, outperforming the state-of-the-art (SOTA). To the best of our knowledge, this is the first method that achieves training-free and continuous fine-grained emotion control in TTS. Demo samples are available at https://emosteer-tts-demo.pages.dev/.