We present Speech-MASSIVE, a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus. Speech-MASSIVE covers 12 languages from different families and inherits from MASSIVE the annotations for the intent prediction and slot-filling tasks. Our extension is prompted by the scarcity of massively multilingual SLU datasets and the growing need for versatile speech datasets to assess foundation models (LLMs, speech encoders) across languages and tasks. We provide a multimodal, multitask, multilingual dataset and report SLU baselines using both cascaded and end-to-end architectures in various training scenarios (zero-shot, few-shot, and full fine-tune). Furthermore, we demonstrate the suitability of Speech-MASSIVE for benchmarking other tasks such as speech transcription, language identification, and speech translation. The dataset, models, and code are publicly available at: https://github.com/hlt-mt/Speech-MASSIVE

本研究针对缺乏大规模多语言语音理解（SLU）数据集的问题，提出了Speech-MASSIVE，一个涵盖12种语言的多语言语音数据集，并包含意图预测和槽填充任务的注释。该数据集为基础模型的评估提供了多模态和多任务支持，其显著发现是适用于多种任务的基准测试，包括语音转录和语言识别。

Speech-MASSIVE：用于SLU及其他领域的多语言语音数据集