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: this https URL.
Abstract (translated)
口语理解（SLU）旨在从用户查询的语音表述中提取语义信息，是任务型对话系统的核心组件。随着深度神经网络模型的显著进步和预训练语言模型的演进，SLU已取得重大突破。然而，由于SLU资源的匮乏，仅有少数高资源语言得以受益于此进展。本文通过引入SLURP-TN数据集来缓解这一障碍：该数据集由55名母语者录制完成，内容源自SLURP数据集中六个领域的句子手工翻译而成的突尼斯方言。最终构建的SLU突尼斯方言数据集包含4165句录音，约5小时声学材料。我们还开发了一系列基于SLURP-TN的自动语音识别与SLU模型。数据集及基线模型已开源，链接如下：此https URL。
URL
https://arxiv.org/abs/2603.21940
