Crafting an effective Automatic Speech Recognition (ASR) solution for dialects demands innovative approaches that not only address the data scarcity issue but also navigate the intricacies of linguistic diversity. In this paper, we address the aforementioned ASR challenge, focusing on the Tunisian dialect. First, textual and audio data is collected and in some cases annotated. Second, we explore self-supervision, semi-supervision and few-shot code-switching approaches to push the state-of-the-art on different Tunisian test sets; covering different acoustic, linguistic and prosodic conditions. Finally, and given the absence of conventional spelling, we produce a human evaluation of our transcripts to avoid the noise coming from spelling inadequacies in our testing references. Our models, allowing to transcribe audio samples in a linguistic mix involving Tunisian Arabic, English and French, and all the data used during training and testing are released for public use and further improvements.

本研究旨在解决突尼斯方言语音识别的挑战，通过采集和注释文本和音频数据，并探索自监督、半监督和少样本代码转换方法，使其在不同的突尼斯测试集中取得前沿成果，最终通过人工评估确保翻译的准确性，所提出的模型适用于突尼斯阿拉伯语、英语和法语的语言混合，并提供训练和测试数据供公众使用和进一步改进。

利用数据收集和无监督学习对突尼斯阿拉伯语混合语音自动识别的提升