As in any data-intensive domain, collecting highquality To fill this gap, this paper explores the use datasets is a fundamental and costly prerequisite of SFMs to automatize the validation of crowdsourced for the development of speech-processing speech data. To this aim, we investigate the applications. Traditional methods heavily rely on employment of off-the-shelf SFMs such as Whisper human workforce, whose costs, as data collection and SeamlessM4T (Radford et al., 2022; Communication scales, are hard to sustain. In the quest for scalable et al., 2023), along with machine translation solutions to tackle this problem, crowdsourcing (MT) models and grapheme-to-phoneme conversion emerged as a viable option that also enables the coverage (G2P). Through experiments on French, of diverse populations (Cefkin et al., 2014; German, and Korean data, we test the integration Poesio et al., 2017). Due to the variable quality of of SFMs and crowdsourcing to reduce validation crowd-sourced data, validation methods that discard costs while preserving final data quality. Our results low-quality contributions are essential to build show that leveraging SFMs yields a cost reduction reliable datasets (Negri et al., 2011; Sabou et al., by over 40%, while maintaining high data quality, 2014; Chittilappilly et al., 2016). This need is exacerbated significantly improving the efficiency and scalability in the collection of speech-text pairs, where of crowd-sourced speech data collection.

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