SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding
This provides a resource for Tunisian dialect SLU, addressing a gap for a low-resource language, but it is incremental as it adapts an existing dataset.
The authors tackled the lack of spoken language understanding resources for low-resource languages by creating SLURP-TN, a dataset of 4165 sentences in Tunisian dialect recorded into about 5 hours of audio, and developed baseline models for it.
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.