# SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding

arXiv: https://arxiv.org/abs/2603.21940 · HTML: https://mlsanity.com/paper/2603.21940/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 23 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [SLURP: A Spoken Language Understanding Resource Package](https://mlsanity.com/paper/2011.13205/) (2020)
- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [Open-Source Conversational AI with SpeechBrain 1.0](https://mlsanity.com/paper/2407.00463/) (2024)
- [Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond](https://mlsanity.com/paper/2408.03900/) (2024)
- [Casablanca: Data and Models for Multidialectal Arabic Speech Recognition](https://mlsanity.com/paper/2410.04527/) (2024)
- [Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers](https://mlsanity.com/paper/2104.01604/) (2021)
- [Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect](https://mlsanity.com/paper/2407.04533/) (2024)
- [SENSE models: an open source solution for multilingual and multimodal semantic-based tasks](https://mlsanity.com/paper/2509.12093/) (2025)
- [TEDxTN: A Three-way Speech Translation Corpus for Code-Switched Tunisian Arabic - English](https://mlsanity.com/paper/2511.10780/) (2025)

## Abstract (arXiv)

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.