ICASSP 2023 – Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech Enhancement

In this episode we discuss Aura: Privacy-preserving Augmentation to Improve Test Set Diversity in Speech Enhancement
by Xavier Gitiaux, Aditya Khant, Ebrahim Beyrami, Chandan Reddy, Jayant Gupchup, Ross Cutler. The paper presents Aura, a privacy-preserving method to enhance test set diversity in speech enhancement models. Usually, these models are trained on public data, which leads to performance issues when applied to customer data due to privacy constraints. Aura addresses this by creating diverse test sets using pre-trained feature extractors and clustering techniques, resulting in improved model rankings and increased test set diversity. The paper introduces the “ears-off” methodology, a generic approach to measure test set diversity, and demonstrates Aura’s effectiveness in speech enhancement tasks.