# One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement

arXiv: https://arxiv.org/abs/2110.10330 · HTML: https://mlsanity.com/paper/2110.10330/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2021.

## Built on by (most cited first, 5 of 5)

- [Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation](https://mlsanity.com/paper/2204.00771/) (2022)
- [ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior](https://mlsanity.com/paper/2505.05657/) (2025)
- [Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation](https://mlsanity.com/paper/2210.12635/) (2022)
- [Challenges and Opportunities in Multi-device Speech Processing](https://mlsanity.com/paper/2206.15432/) (2022)
- [Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation](https://mlsanity.com/paper/2211.02944/) (2022)

## Abstract (arXiv)

With the recent surge of video conferencing tools usage, providing high-quality speech signals and accurate captions have become essential to conduct day-to-day business or connect with friends and families. Single-channel personalized speech enhancement (PSE) methods show promising results compared with the unconditional speech enhancement (SE) methods in these scenarios due to their ability to remove interfering speech in addition to the environmental noise. In this work, we leverage spatial information afforded by microphone arrays to improve such systems' performance further. We investigate the relative importance of speaker embeddings and spatial features. Moreover, we propose a new causal array-geometry-agnostic multi-channel PSE model, which can generate a high-quality enhanced signal from arbitrary microphone geometry. Experimental results show that the proposed geometry agnostic model outperforms the model trained on a specific microphone array geometry in both speech quality and automatic speech recognition accuracy. We also demonstrate the effectiveness of the proposed approach for unseen array geometries.