Many self-supervised speech models, varying in their pre-training objective, input modality, and pre-training data, have been proposed in the last few years. Despite impressive successes on downstream tasks, we still have a limited understanding of the properties encoded by the models and the differences across models. In this work, we examine the intermediate representations for a variety of recent models. Specifically, we measure acoustic, phonetic, and word-level properties encoded in individual layers, using a lightweight analysis tool based on canonical correlation analysis (CCA). We find that these properties evolve across layers differently depending on the model, and the variations relate to the choice of pre-training objective. We further investigate the utility of our analyses for downstream tasks by comparing the property trends with performance on speech recognition and spoken language understanding tasks. We discover that CCA trends provide reliable guidance to choose layers of interest for downstream tasks and that single-layer performance often matches or improves upon using all layers, suggesting implications for more efficient use of pre-trained models.

翻译：许多自监督语音模型在过去几年中被提出，它们的预训练目标、输入模态和预训练数据各不相同。尽管在下游任务中取得了惊人的成功，但我们对模型编码的属性和模型之间的差异仍知之甚少。在本文中，我们研究了各种最近模型的中间表示形式。具体来说，我们使用基于规范相关分析（CCA）的轻量级分析工具测量了单个层中编码的声学、语音和单词级属性。我们发现，这些属性在不同的模型中会以不同的方式在层之间演变，而这些差异与预训练目标的选择有关。我们进一步探究了我们的分析在下游任务中的实用性，通过比较属性趋势与语音识别和口语理解任务的性能。我们发现，CCA趋势提供了选择感兴趣层的可靠指导，并且单层性能常常与使用所有层相当甚至更好，这表明有关预训练模型更有效的使用的启示。