The recent success of Automatic Speech Recognition (ASR) is largely
attributed to the ever-growing amount of training data. However, this trend has
made model training prohibitively costly and imposed computational demands.
While data pruning has been proposed to mitigate this issue by identifying a
small subset of relevant data, its application in ASR has been barely explored,
and existing works often entail significant overhead to achieve meaningful
results. To fill this gap, this paper presents the first investigation of
dynamic data pruning for ASR, finding that we can reach the full-data
performance by dynamically selecting 70% of data. Furthermore, we introduce
Dynamic Data Pruning for ASR (DDP-ASR), which offers several fine-grained
pruning granularities specifically tailored for speech-related datasets, going
beyond the conventional pruning of entire time sequences. Our intensive
experiments show that DDP-ASR can save up to 1.6x training time with negligible
performance loss.

本研究首次探索了自动语音识别领域中的动态数据修剪方法（DDP-ASR），通过动态选择 70％的数据，实现了与全部数据训练相当的性能，同时为语音相关数据集提供了细粒度的修剪选择，节省了高达 1.6 倍的训练时间。

自动语音识别的动态数据修剪

Dynamic Data Pruning for Automatic Speech Recognition

Finetuning large language models inflates the costs of NLU applications and
remains the bottleneck of development cycles. Recent works in computer vision
use data pruning to reduce training time. Pruned data selection with static
methods is based on a score calculated for each training example prior to
finetuning, which involves important computational overhead. Moreover, the
score may not necessarily be representative of sample importance throughout the
entire training duration. We propose to address these issues with a refined
version of dynamic data pruning, a curriculum which periodically scores and
discards unimportant examples during finetuning. Our method leverages an EL2N
metric that we extend to the joint intent and slot classification task, and an
initial finetuning phase on the full train set. Our results on the GLUE
benchmark and four joint NLU datasets show a better time-accuracy trade-off
compared to static methods. Our method preserves full accuracy while training
on 50% of the data points and reduces computational times by up to 41%. If we
tolerate instead a minor drop of accuracy of 1%, we can prune 80% of the
training examples for a reduction in finetuning time reaching 66%.

本研究提出了一种基于动态数据修剪的方法，使用 EL2N 度量和初始微调阶段，在保持完整准确性的同时，可将数据量减少 50%~80% 以大大减少微调时间，并在 GLUE 基准测试和四个联合 NLU 数据集上展现出更好的时间 - 准确性平衡。