3 papers
cs.CR2024
Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping
Lun Wang, Om Thakkar, Zhong Meng +3
Gradient clipping plays a vital role in training large-scale automatic speech recognition (ASR) models. It is typically applied to minibatch gradients to prevent gradient explosion…
cs.LG2024
Noise Masking Attacks and Defenses for Pretrained Speech Models
Matthew Jagielski, Om Thakkar, Lun Wang
Speech models are often trained on sensitive data in order to improve model performance, leading to potential privacy leakage. Our work considers noise masking attacks, introduced…
cs.LG2023
Unintended Memorization in Large ASR Models, and How to Mitigate It
Lun Wang, Om Thakkar, Rajiv Mathews
It is well-known that neural networks can unintentionally memorize their training examples, causing privacy concerns. However, auditing memorization in large non-auto-regressive au…