# Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyond

arXiv: https://arxiv.org/abs/2408.03900 · HTML: https://mlsanity.com/paper/2408.03900/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 23 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 19 of 19)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [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)
- [Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates](https://mlsanity.com/paper/1911.03167/) (2019)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [End-to-End Slot Alignment and Recognition for Cross-Lingual NLU](https://mlsanity.com/paper/2004.14353/) (2020)
- [CoVoST 2 and Massively Multilingual Speech-to-Text Translation](https://mlsanity.com/paper/2007.10310/) (2020)
- [MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible](https://mlsanity.com/paper/1907.12895/) (2019)
- [MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2212.10455/) (2022)
- [ITALIC: An Italian Intent Classification Dataset](https://mlsanity.com/paper/2306.08502/) (2023)

## Built on by (most cited first, 20 of 20)

- [Massive Sound Embedding Benchmark (MSEB)](https://mlsanity.com/paper/2602.07143/) (2026)
- [SpeechTaxi: On Multilingual Semantic Speech Classification](https://mlsanity.com/paper/2409.06372/) (2024)
- [SENSE models: an open source solution for multilingual and multimodal semantic-based tasks](https://mlsanity.com/paper/2509.12093/) (2025)
- [Exploring Fine-Tuning of Large Audio Language Models for Spoken Language Understanding under Limited Speech Data](https://mlsanity.com/paper/2509.15389/) (2025)
- [Prepending or Cross-Attention for Speech-to-Text? An Empirical Comparison](https://mlsanity.com/paper/2501.02370/) (2025)
- ["Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding](https://mlsanity.com/paper/2505.15700/) (2025)
- [Exploring Generative Error Correction for Dysarthric Speech Recognition](https://mlsanity.com/paper/2505.20163/) (2025)
- [Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects](https://mlsanity.com/paper/2510.07890/) (2025)
- [GlobeAudio: A Multilingual Multicultural Benchmark for Naturalistic Evaluation of Large Audio-Language Models](https://mlsanity.com/paper/2606.08194/) (2026)
- [Speech Foundation Models and Crowdsourcing for Efficient, High-Quality Data Collection](https://mlsanity.com/paper/2412.11978/) (2024)
- [TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild](https://mlsanity.com/paper/2603.21478/) (2026)
- [WorldSpeech: A Multilingual Speech Corpus from Around the World](https://mlsanity.com/paper/2605.09167/) (2026)
- [Selective Capability Unlearning in End-to-End Spoken Language Understanding](https://mlsanity.com/paper/2606.24063/) (2026)
- [Aslema at NADI 2026: Data Augmentation for Intent Recognition and Slot Filling](https://mlsanity.com/paper/2608.18689/) (2026)
- ["KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understanding](https://mlsanity.com/paper/2505.20176/) (2025)
- [SLURP-TN : Resource for Tunisian Dialect Spoken Language Understanding](https://mlsanity.com/paper/2603.21940/) (2026)
- [Benchmarking LLMs on the Massive Sound Embedding Benchmark (MSEB)](https://mlsanity.com/paper/2605.04556/) (2026)
- [Does Translation-Enhanced Speech Encoder Pre-training Affect Speech LLMs?](https://mlsanity.com/paper/2606.25444/) (2026)
- [NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2609.27086/) (2026)
- [τ-Multilingual: Benchmarking Voice Agents Across Languages](https://mlsanity.com/paper/2609.35820/) (2026)

## Abstract (arXiv)

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