Self-supervised pretraining on speech data has achieved a lot of progress.
High-fidelity representation of the speech signal is learned from a lot of
untranscribed data and shows promising performance. Recently, there are several
works focusing on evaluating the quality of self-supervised pretrained
representations on various tasks without domain restriction, e.g. SUPERB.
However, such evaluations do not provide a comprehensive comparison among many
ASR benchmark corpora. In this paper, we focus on the general applications of
pretrained speech representations, on advanced end-to-end automatic speech
recognition (E2E-ASR) models. We select several pretrained speech
representations and present the experimental results on various open-source and
publicly available corpora for E2E-ASR. Without any modification of the
back-end model architectures or training strategy, some of the experiments with
pretrained representations, e.g., WSJ, WSJ0-2mix with HuBERT, reach or
outperform current state-of-the-art (SOTA) recognition performance. Moreover,
we further explore more scenarios for whether the pretraining representations
are effective, such as the cross-language or overlapped speech. The scripts,
configuratons and the trained models have been released in ESPnet to let the
community reproduce our experiments and improve them.

本文探讨了预训练语音模型在 E2E-ASR 中的潜在应用，发现在一些 ASR benchmark corpora 上，使用预训练模型能够超越当前最先进的识别性能。其中，HuBERT 模型表现尤为突出，实验代码和模型参数已开源。