AURA: Privacy-Preserving Augmentation to Improve Test Set Diversity in Speech Enhancement

• Main Authors: Gitiaux, Xavier, Khant, Aditya, Beyrami, Ebrahim, Reddy, Chandan, Gupchup, Jayant, Cutler, Ross
• Format: Conference Proceeding
• Language: English
• Subjects: Echo cancellers; Noise reduction; Ontologies; Privacy; Production; Signal processing; Speech enhancement; test set
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP49357.2023.10096879

Speech enhancement models running in production environments are commonly trained on publicly available data. This approach leads to regressions due to the lack of training/testing on representative customer data. Moreover, due to privacy reasons, developers cannot listen to customer content. This 'ears-off' situation motivates Aura, an end-to-end solution to make existing speech enhancement train and test sets more challenging and diverse while being sample efficient. Aura is 'ears-off' because it relies on a feature extractor and metrics of speech quality, DNSMOS P.835, and AECMOS, that are pre-trained on data obtained from public sources. We evalaute Aura on two speech enhancement tasks: noise suppression (NS) and audio echo cancellation (AEC). Aura samples an NS test set 0.42 harder in terms of P.835 OVRL than random sampling; and, an AEC test set 1.93 harder in AECMOS. Moreover, Aura increases diversity by 30% for NS tasks and by 530% for AEC tasks compared to greedy sampling. Moreover, Aura achieves a 26% improvement in Spearman's rank correlation coefficient (SRCC) compared to random sampling when used to stack rank NS models.