Evaluating and Improving the Robustness of Speech Command Recognition Models to Noise and Distribution Shifts

ABSTRACT Although prior work in computer vision has shown strong correlations between in-distribution (ID) and out-of-distribution (OOD) accuracies, such relationships remain underexplored in audio-based models. In this study, we investigate how training conditions and input features affect the robustness and generalization abilities of spoken keyword classifiers under OOD conditions. To quantify the impact of noise on generalization, we make use of two metrics: Fairness (F), which measures overall accuracy gains compared to a baseline model, and Robustness (R), which assesses the convergence between ID and OOD performance. Our results suggest that noise-aware training improves robustness in some configurations. These findings shed new light on the benefits and limitations of noise-based augmentation for generalization in speech models. Index T erms-- accuracy on the line, command recognition, OOD generalization, noise robustness, speech features 1. INTRODUCTION In recent years, deep learning models have achieved remarkable performance in Automatic Speech Recognition (ASR) and Spoken Command Recognition (SCR) tasks.