We investigate robustness properties of pre-trained neural models for
automatic speech recognition. Real life data in machine learning is usually
very noisy and almost never clean, which can be attributed to various factors
depending on the domain, e.g. outliers, random noise and adversarial noise.
Therefore, the models we develop for various tasks should be robust to such
kinds of noisy data, which led to the thriving field of robust machine
learning. We consider this important issue in the setting of automatic speech
recognition. With the increasing popularity of pre-trained models, it's an
important question to analyze and understand the robustness of such models to
noise. In this work, we perform a robustness analysis of the pre-trained neural
models wav2vec2, HuBERT and DistilHuBERT on the LibriSpeech and TIMIT datasets.
We use different kinds of noising mechanisms and measure the model performances
as quantified by the inference time and the standard Word Error Rate metric. We
also do an in-depth layer-wise analysis of the wav2vec2 model when injecting
noise in between layers, enabling us to predict at a high level what each layer
learns. Finally for this model, we visualize the propagation of errors across
the layers and compare how it behaves on clean versus noisy data. Our
experiments conform the predictions of Pasad et al. [2021] and also raise
interesting directions for future work.

本文研究了预训练神经模型在自动语音识别中的鲁棒性，并对 wav2vec2，HuBERT 和 DistilHuBERT 进行了鲁棒性分析，发现它们在 LibriSpeech 和 TIMIT 数据集上对噪声的鲁棒性不同，同时进行了层次分析以预测每层的学习，通过误差传播和对比清晰和嘈杂的数据，验证了 Pasad 等人的预测，并提出未来研究的有趣方向。