CAFE: Spontaneous code-switching speech dataset in Algerian dialect, French and English
Year: 2025
Type: data-paper
Abstract: Publicly available datasets capturing spontaneous multilingual speech—especially those involving code-switching between Algerian Arabic, French, and English—are critically scarce. This lack of resources hinders the development of automatic speech recognition (ASR) and multilingual NLP systems for low-resource languages and under-represented Arabic dialects. We introduce CAFE, a novel dataset comprising approximately 37 hours of spontaneous, in vivo human–human conversations among 100+ speakers across Algeria. The dialogues cover diverse everyday topics such as sports, science, and technology, and exhibit a rich range of natural conversational phenomena, including explicit code-switching, overlapping speech, non-lexical vocalizations (e.g., laughter, fillers, ambient noise), and dialectal variation reflecting Algeria’s sociolinguistic landscape. CAFE is released in two tiers: CAFE-small (2h 36m): A fully human-annotated subset with high-quality transcriptions, vocal event labels, and linguistic annotations, supporting ASR evaluation, NLP tasks, and code-switching analysis. CAFE-large (∼34h 35m): The remainder of the corpus, automatically labeled, suitable for pretraining and semi-supervised learning. To support controlled experiments, CAFE-small includes two curated subsets: (i) CAFE-small-clean (2h 18m): Contains utterances with no overlapping speech. (ii) CAFE-small-overlap (17m): Contains 23 files with overlap segments and timestamps. The dataset also provides rich metadata, including audio chunk IDs, dialect labels, and both raw and linguistically processed transcripts. CAFE offers a valuable resource for advancing ASR, dialect identification, and sociolinguistic analysis in multilingual and low-resource settings.
Source: Data in Brief
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,220
OpenAlex ID: W4414977604