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.

渐变剪切在训练大规模自动语音识别（ASR）模型中起着至关重要的作用。本研究系统地调查了一种特定细粒度的渐变剪切方法，即每个核心剪切（PCC），对于训练各种 ASR 模型的影响。我们经验证明，PCC 可以有效地减轻 ASR 模型中的非预期记忆。令人惊讶的是，我们发现 PCC 对于 ASR 性能指标产生了积极影响，提高了收敛速度并降低了词错误率。为了避免调整 PCC 引入的额外超参数，我们进一步提出了一种新的变种，自适应每个核心剪切（APCC），用于简化优化。我们的研究结果突显了 PCC 作为一种稳健、保护隐私的 ASR 模型训练策略的多方面优势。