  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, and to the individual sample gradients to mitigate
unintended memorization. This work systematically investigates the impact of a
specific granularity of gradient clipping, namely per-core clip-ping (PCC),
across training a wide range of ASR models. We empirically demonstrate that PCC
can effectively mitigate unintended memorization in ASR models. Surprisingly,
we find that PCC positively influences ASR performance metrics, leading to
improved convergence rates and reduced word error rates. To avoid tuning the
additional hyperparameter introduced by PCC, we further propose a novel
variant, adaptive per-core clipping (APCC), for streamlined optimization. Our
findings highlight the multifaceted benefits of PCC as a strategy for robust,
privacy-forward ASR model training.
