Speech encoders pretrained through self-supervised learning (SSL) have demonstrated remarkable performance in various downstream tasks, including Spoken Language Understanding (SLU) and Automatic Speech Recognition (ASR). For instance, fine-tuning SSL models for such tasks has shown significant potential, leading to improvements in the SOTA performance across challenging datasets. In contrast to existing research, this paper contributes by comparing the effectiveness of SSL approaches in the context of (i) the low-resource spoken Tunisian Arabic dialect and (ii) its combination with a low-resource SLU and ASR scenario, where only a few semantic annotations are available for fine-tuning. We conduct experiments using many SSL speech encoders on the TARIC-SLU dataset. We use speech encoders that were pre-trained on either monolingual or multilingual speech data. Some of them have also been refined without in-domain nor Tunisian data through multimodal supervised teacher-student paradigm. This study yields numerous significant findings that we are discussing in this paper.

本论文研究通过自监督学习进行预训练的语音编码器在不同领域的下游任务中表现出卓越的性能，包括口语语言理解（SLU）和自动语音识别（ASR）。针对低资源的突尼斯阿拉伯口音情境以及结合了低资源SLU和ASR场景的情况，本研究通过比较不同的自监督学习方法的有效性做出贡献。使用许多经过自监督学习训练的语音编码器在TARIC-SLU数据集上进行实验，其中语音编码器均在单语言或多语言语音数据上进行了预训练。一些语音编码器还通过多模式监督师生范式进行了改进，而无需领域内数据或突尼斯数据。本研究得出了许多重要发现，并在本文中进行了讨论。

突尼斯方言的低资源语言理解和自动语音识别的语音编码器性能分析