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

Leveraging Data Collection and Unsupervised Learning for Code-switched  Tunisian Arabic Automatic Speech Recognition

Tunisians on social media tend to express themselves in their local dialect
using Latin script (TUNIZI). This raises an additional challenge to the process
of exploring and recognizing online opinions. To date, very little work has
addressed TUNIZI sentiment analysis due to scarce resources for training an
automated system. In this paper, we focus on the Tunisian dialect sentiment
analysis used on social media. Most of the previous work used machine learning
techniques combined with handcrafted features. More recently, Deep Neural
Networks were widely used for this task, especially for the English language.
In this paper, we explore the importance of various unsupervised word
representations (word2vec, BERT) and we investigate the use of Convolutional
Neural Networks and Bidirectional Long Short-Term Memory. Without using any
kind of handcrafted features, our experimental results on two publicly
available datasets showed comparable performances to other languages.

本文探讨了在社交媒体上使用突尼斯方言进行情感分析的挑战和方法，重点介绍了使用无监督学习下的自然语言处理和深度神经网络技术，在不使用手工特征的情况下，成功实现了与其他语言情感分析的相当水平。