  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.
