Self-supervised learning (SSL)-based speech models are extensively used for
full-stack speech processing. However, it has been observed that improving
SSL-based speech representations using unlabeled speech for content-related
tasks is challenging and computationally expensive. Recent attempts have been
made to address this issue with cost-effective self-supervised fine-tuning
(SSFT) approaches. Continuing in this direction, a cost-effective SSFT method
named "LASER: Learning by Aligning Self-supervised Representations" is
presented. LASER is based on the soft-DTW alignment loss with temporal
regularisation term. Experiments are conducted with HuBERT and WavLM models and
evaluated on the SUPERB benchmark for two content-related tasks: automatic
speech recognition (ASR) and phoneme recognition (PR). A relative improvement
of 3.7% and 8.2% for HuBERT, and 4.1% and 11.7% for WavLM are observed, for the
ASR and PR tasks respectively, with only < 3 hours of fine-tuning on a single
GPU.

使用 SSFT 方法 LASER，通过自附着自监督表示的学习和对齐，通过少量的 GPU 精调获得相对于 ASR 和 PR 任务的显著改进。

LASER：通过自我监督学习对齐语音表示以改进内容相关任务

LASER: Learning by Aligning Self-supervised Representations of Speech  for Improving Content-related Tasks

There is a growing interest in cost-effective self-supervised fine-tuning
(SSFT) of self-supervised learning (SSL)-based speech models to obtain
task-specific representations. These task-specific representations are used for
robust performance on various downstream tasks by fine-tuning on the labelled
data. This work presents a cost-effective SSFT method named Self-supervised
Correspondence (SCORE) fine-tuning to adapt the SSL speech representations for
content-related tasks. The proposed method uses a correspondence training
strategy, aiming to learn similar representations from perturbed speech and
original speech. Commonly used data augmentation techniques for content-related
tasks (ASR) are applied to obtain perturbed speech. SCORE fine-tuned HuBERT
outperforms the vanilla HuBERT on SUPERB benchmark with only a few hours of
fine-tuning (< 5 hrs) on a single GPU for automatic speech recognition, phoneme
recognition, and query-by-example tasks, with relative improvements of 1.09%,
3.58%, and 12.65%, respectively. SCORE provides competitive results with the
recently proposed SSFT method SPIN, using only 1/3 of the processed speech
compared to SPIN.

提出了一种称为 SCORE 微调的自我监督一致性微调方法，用于自适应自我监督学习语音表示，以适应与内容相关的任务。与 SPIN 方法相比，SCORE 仅使用 1/3 的处理语音，在超级基准（SUPERB benchmark）上表现出优越的结果。(Translated simplified Chinese summary)