TEDxTN: A Three-way Speech Translation Corpus for Code-Switched Tunisian Arabic - English

2025-11-13

Proceedings of The Third Arabic Natural Language Processing Conference

AI Summary

Data scarcity severely hampers Tunisian Arabic–English speech translation, particularly due to the absence of publicly available code-switched speech corpora, impeding low-resource dialectal NLP research. To address this, we introduce TEDxTN—the first open-source, code-switched Tunisian Arabic–English speech translation corpus—covering speakers from 11+ regions in Tunisia, 108 TEDx talks, and 25 hours of audio. The corpus features professional segmentation, bilingual transcription, human translation, and a publicly released annotation guideline. Methodologically, we present the first end-to-end joint modeling framework for automatic speech recognition and speech translation, leveraging pretrained models fine-tuned on TEDxTN to establish a strong, reproducible baseline. TEDxTN fills a critical gap in Arabic dialectal speech translation resources and serves as a foundational benchmark for low-resource spoken language translation, code-switching modeling, and dialectal NLP.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this paper, we introduce TEDxTN, the first publicly available Tunisian Arabic to English speech translation dataset. This work is in line with the ongoing effort to mitigate the data scarcity obstacle for a number of Arabic dialects. We collected, segmented, transcribed and translated 108 TEDx talks following our internally developed annotations guidelines. The collected talks represent 25 hours of speech with code-switching that cover speakers with various accents from over 11 different regions of Tunisia. We make the annotation guidelines and corpus publicly available. This will enable the extension of TEDxTN to new talks as they become available. We also report results for strong baseline systems of Speech Recognition and Speech Translation using multiple pre-trained and fine-tuned end-to-end models. This corpus is the first open source and publicly available speech translation corpus of Code-Switching Tunisian dialect. We believe that this is a valuable resource that can motivate and facilitate further research on the natural language processing of Tunisian Dialect.

Problem

• Developing first Tunisian Arabic-English speech translation corpus
• Addressing data scarcity for Arabic dialect processing
• Providing code-switched speech resources for NLP research

Innovation

• First Tunisian Arabic-English speech translation dataset
• Collected 108 TEDx talks with code-switching annotations
• Used pre-trained end-to-end models for baseline systems