TEDxTN: A Three-way Speech Translation Corpus for Code-Switched Tunisian Arabic - English
This work addresses the data scarcity issue for researchers in natural language processing focusing on Tunisian Arabic, though it is incremental as it builds on existing efforts for Arabic dialects.
The authors tackled the problem of data scarcity for Tunisian Arabic to English speech translation by creating TEDxTN, the first publicly available corpus for this task, comprising 25 hours of code-switched speech from 108 TEDx talks. They reported baseline results for speech recognition and translation using pre-trained models, achieving initial performance metrics to facilitate further research.
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.