The paper introduces and publicly releases (Data download link available after acceptance) CAFE -- the first Code-switching dataset between Algerian dialect, French, and english languages. The CAFE speech data is unique for (a) its spontaneous speaking style in vivo human-human conversation capturing phenomena like code-switching and overlapping speech, (b) addresses distinct linguistic challenges in North African Arabic dialect; (c) the CAFE captures dialectal variations from various parts of Algeria within different sociolinguistic contexts. CAFE data contains approximately 37 hours of speech, with a subset, CAFE-small, of 2 hours and 36 minutes released with manual human annotation including speech segmentation, transcription, explicit annotation of code-switching points, overlapping speech, and other events such as noises, and laughter among others. The rest approximately 34.58 hours contain pseudo label transcriptions. In addition to the data release, the paper also highlighted the challenges of using state-of-the-art Automatic Speech Recognition (ASR) models such as Whisper large-v2,3 and PromptingWhisper to handle such content. Following, we benchmark CAFE data with the aforementioned Whisper models and show how well-designed data processing pipelines and advanced decoding techniques can improve the ASR performance in terms of Mixed Error Rate (MER) of 0.310, Character Error Rate (CER) of 0.329 and Word Error Rate (WER) of 0.538.

本文介绍并公开发布了CAFE，这是首个阿尔及利亚方言、法语和英语之间的代码切换数据集。该数据集通过捕捉自发对话中的代码切换和重叠发言，聚焦北非阿拉伯方言的独特语言挑战，显示出高效的数据处理和解码技术可以显著改善自动语音识别模型的性能，混合错误率达到0.310。

CAFE：一种新型阿尔及利亚方言法语与英语的代码切换数据集