Acoustic Feature Learning for Robust Speaker Verification Under Mismatched Noise and Recording Conditions

Abstract

Speaker verification is crucial in biometric security systems, but performance degradation occurs under mismatched noise and recording conditions. This paper explores acoustic feature learning techniques to enhance robustness in speaker verification systems. We analyze state-of-the-art methods, recent advances from 2023, and novel feature learning strategies, including deep learning models. Empirical evaluations demonstrate the effectiveness of selected acoustic features under diverse noise scenarios. The study provides comparative analyses through tables and graphs, offering insights into optimal feature learning strategies.