SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding

2026-03-23

AI Summary

This work addresses the scarcity of high-quality spoken language understanding resources for Tunisian Arabic, which has severely hindered its deployment in task-oriented dialogue systems. To bridge this gap, the authors introduce SLURP-TN, the first spoken language understanding dataset for Tunisian dialect, comprising approximately five hours of speech from 55 native speakers across six domains, totaling 4,165 annotated utterances. Data quality is ensured through a combination of human translation and native-speaker recordings. The study further develops baseline automatic speech recognition (ASR) and spoken language understanding (SLU) systems leveraging deep neural networks and pretrained language models. Both the dataset and associated models are publicly released, filling a critical void in structured SLU benchmarks for low-resource dialects and significantly advancing research and applications of Tunisian Arabic in spoken interactive systems.

Technology Category

• Natural Language Processing: Speech
• 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
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Spoken Language Understanding (SLU) aims to extract the semantic information from the speech utterance of user queries. It is a core component in a task-oriented dialogue system. With the spectacular progress of deep neural network models and the evolution of pre-trained language models, SLU has obtained significant breakthroughs. However, only a few high-resource languages have taken advantage of this progress due to the absence of SLU resources. In this paper, we seek to mitigate this obstacle by introducing SLURP-TN. This dataset was created by recording 55 native speakers uttering sentences in Tunisian dialect, manually translated from six SLURP domains. The result is an SLU Tunisian dialect dataset that comprises 4165 sentences recorded into around 5 hours of acoustic material. We also develop a number of Automatic Speech Recognition and SLU models exploiting SLUTP-TN. The Dataset and baseline models are available at: https://huggingface.co/datasets/Elyadata/SLURP-TN.

Problem

• Spoken Language Understanding
• Tunisian dialect
• low-resource languages
• SLU dataset
• task-oriented dialogue system

Innovation

• Spoken Language Understanding
• Tunisian Dialect
• Low-resource Languages
• SLURP-TN
• Automatic Speech Recognition