# I2CR: Improving Noise Robustness on Keyword Spotting Using Inter-Intra Contrastive Regularization

arXiv: https://arxiv.org/abs/2209.06360 · HTML: https://mlsanity.com/paper/2209.06360/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2022.

## Built on by (most cited first, 4 of 4)

- [On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems](https://mlsanity.com/paper/2403.10549/) (2024)
- [HuBERT-VIC: Improving Noise-Robust Automatic Speech Recognition of Speech Foundation Model via Variance-Invariance-Covariance Regularization](https://mlsanity.com/paper/2508.12292/) (2025)
- [Explainable Disentanglement on Discrete Speech Representations for Noise-Robust ASR](https://mlsanity.com/paper/2510.25150/) (2025)
- [Noise-Agnostic Multitask Whisper Training for Reducing False Alarm Errors in Call-for-Help Detection](https://mlsanity.com/paper/2501.11631/) (2025)

## Abstract (arXiv)

Noise robustness in keyword spotting remains a challenge as many models fail to overcome the heavy influence of noises, causing the deterioration of the quality of feature embeddings. We proposed a contrastive regularization method called Inter-Intra Contrastive Regularization (I2CR) to improve the feature representations by guiding the model to learn the fundamental speech information specific to the cluster. This involves maximizing the similarity across Intra and Inter samples of the same class. As a result, it pulls the instances closer to more generalized representations that form more prominent clusters and reduces the adverse impact of noises. We show that our method provides consistent improvements in accuracy over different backbone model architectures under different noise environments. We also demonstrate that our proposed framework has improved the accuracy of unseen out-of-domain noises and unseen variant noise SNRs. This indicates the significance of our work with the overall refinement in noise robustness.