# Loud and Clear: Dynamic Activation Steering for Improving Speech Intelligibility in Noisy Environments

arXiv: https://arxiv.org/abs/2610.07647 · HTML: https://mlsanity.com/paper/2610.07647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Activation steering methods, one of 13 directions in Mechanistic interpretability.
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 6 Oct 2026.

## Placed in

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

## Builds on (most cited first, 10 of 10)

- [Steering Language Models With Activation Engineering](https://mlsanity.com/paper/2308.10248/) (2023)
- [Qwen3-TTS Technical Report](https://mlsanity.com/paper/2601.15621/) (2026)
- [EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2406.06185/) (2024)
- [EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering](https://mlsanity.com/paper/2508.03543/) (2025)
- [CoCoEmo: Composable and Controllable Human-Like Emotional TTS via Activation Steering](https://mlsanity.com/paper/2602.03420/) (2026)
- [Prompt-Activation Duality: Improving Activation Steering via Attention-Level Interventions](https://mlsanity.com/paper/2605.10664/) (2026)
- [Activation Steering for Accent-Neutralized Zero-Shot Text-To-Speech](https://mlsanity.com/paper/2603.05977/) (2026)
- [Analysis and Synthesis of Hypo and Hyperarticulated Speech](https://mlsanity.com/paper/2006.04136/) (2020)
- [Voice Conversion for Lombard Speaking Style with Implicit and Explicit Acoustic Feature Conditioning](https://mlsanity.com/paper/2507.09310/) (2025)
- [Synthesizing the Lombard Effect: Multi-Level Control of Speech Clarity and Vocal Effort in TTS](https://mlsanity.com/paper/2606.23176/) (2026)

## Abstract (arXiv)

Speech becomes less intelligible in noisy environments, and humans naturally adapt their voice to compensate. Inspired by this behavior, we investigate whether a text-to-speech (TTS) model can be guided to produce more intelligible speech using activation steering, without retraining. We focus on two characteristics of the Lombard effect: increased vocal effort and hyper-articulation. We introduce a prompt-relative steering mechanism that prevents steering effects from accumulating during generation while allowing their strength to be adjusted dynamically. Across seen and unseen speakers and multiple languages, our method produces systematic changes in Lombard-related acoustic features, preserves speaker similarity (89-95%), and reduces WER under background noise by 7-22% at 1 dB SNR. These results show that pretrained TTS models can be dynamically controlled to generate more intelligible speech without retraining.