# Attention does not guarantee best performance in speech enhancement

arXiv: https://arxiv.org/abs/2302.05690 · HTML: https://mlsanity.com/paper/2302.05690/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 11 Feb 2023.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 4 of 4)

- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [ICASSP 2022 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2202.13288/) (2022)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques](https://mlsanity.com/paper/2206.05876/) (2022)

## Abstract (arXiv)

Attention mechanism has been widely utilized in speech enhancement (SE) because theoretically it can effectively model the long-term inherent connection of signal both in time domain and spectrum domain. However, the generally used global attention mechanism might not be the best choice since the adjacent information naturally imposes more influence than the far-apart information in speech enhancement. In this paper, we validate this conjecture by replacing attention with RNN in two typical state-of-the-art (SOTA) models, multi-scale temporal frequency convolutional network (MTFAA) with axial attention and conformer-based metric-GAN network (CMGAN).