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SLURP: A Spoken Language Understanding Resource Package

arXiv 2011.13205 ↗First posted 26 Nov 2020cs.CL cs.LGcited by 197

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Abstract (arXiv)

Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https: //github.com/pswietojanski/slurp.

Key facts. Cited by 197 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Nov 2020.

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Citing papers per year: 2021: 10; 2022: 32; 2023: 50; 2024: 44; 2025: 33; 2026: 28.

197 papers.

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  1. 2023SALMONN: Towards Generic Hearing Abilities for Large Language Modelscited by 492
  2. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
  3. 2024AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehensioncited by 215
  4. 2022Comparative layer-wise analysis of self-supervised speech modelscited by 155
  5. 2022MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languagescited by 142
  6. 2024WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modelingcited by 124
  7. 2025MMSU: A Massive Multi-task Spoken Language Understanding and Reasoning Benchmarkcited by 101
  8. 2023Textually Pretrained Speech Language Modelscited by 99
  9. 2022Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understandingcited by 90
  10. 2022E-Branchformer: Branchformer with Enhanced merging for speech recognitioncited by 90
  11. 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
  12. 2023LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPTcited by 77
38 more
  1. 2024Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Modelcited by 70
  2. 2024SpeechVerse: A Large-scale Generalizable Audio Language Modelcited by 62
  3. 2023Efficient Sequence Transduction by Jointly Predicting Tokens and Durationscited by 56
  4. 2021ESPnet-SLU: Advancing Spoken Language Understanding through ESPnetcited by 53
  5. 2024WorkBench: a Benchmark Dataset for Agents in a Realistic Workplace Settingcited by 47
  6. 2023Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Studycited by 42
  7. 2025Recent Advances in Discrete Speech Tokens: A Reviewcited by 42
  8. 2022SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Taskscited by 34
  9. 2024DASB - Discrete Audio and Speech Benchmarkcited by 32
  10. 2022LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Taggingcited by 29
  11. 2023Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understandingcited by 29
  12. 2023Speech Self-Supervised Representation Benchmarking: Are We Doing it Right?cited by 28
  13. 2025InteractiveOmni: A Unified Omni-modal Model for Audio-Visual Multi-turn Dialoguecited by 27
  14. 2022WAVPROMPT: Towards Few-Shot Spoken Language Understanding with Frozen Language Modelscited by 25
  15. 2024Speech Translation with Speech Foundation Models and Large Language Models: What is There and What is Missing?cited by 23
  16. 2021Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understandingcited by 22
  17. 2022STOP: A dataset for Spoken Task Oriented Semantic Parsingcited by 22
  18. 2023Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understandingcited by 21
  19. 2023Can Contextual Biasing Remain Effective with Whisper and GPT-2?cited by 21
  20. 2022Integrating Text Inputs For Training and Adapting RNN Transducer ASR Modelscited by 20
  21. 2024Speech-MASSIVE: A Multilingual Speech Dataset for SLU and Beyondcited by 20
  22. 2024Improving Semantic Understanding in Speech Language Models via Brain-tuningcited by 20
  23. 2025AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluationcited by 20
  24. 2021Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Networkcited by 19
  25. 2023NLU on Data Diets: Dynamic Data Subset Selection for NLP Classification Taskscited by 19
  26. 2024Exploring the Capability of Mamba in Speech Applicationscited by 19
  27. 2022A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understandingcited by 18
  28. 2023SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understandingcited by 18
  29. 2023CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Taskscited by 18
  30. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 16
  31. 2023Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Headscited by 16
  32. 2022MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialoguecited by 15
  33. 2022A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialogcited by 14
  34. 2024Paralinguistics-Aware Speech-Empowered Large Language Models for Natural Conversationcited by 14
  35. 2024Benchmarking Representations for Speech, Music, and Acoustic Eventscited by 14
  36. 2024Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognitioncited by 14
  37. 2025SIFT-50M: A Large-Scale Multilingual Dataset for Speech Instruction Fine-Tuningcited by 14
  38. 2023A Comparative Study on E-Branchformer vs Conformer in Speech Recognition, Translation, and Understanding Taskscited by 13

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