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SLURP: A Spoken Language Understanding Resource Package

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arxiv 2011.13205 v1 pith:XUFXY2J3 submitted 2020-11-26 cs.CL cs.LG

Emanuele Bastianelli , Andrea Vanzo , Pawel Swietojanski , Verena Rieser This is my paper · ORCID
classification cs.CLcs.LG
keywords slurpavailableerrorlanguagepackagespokenunderstandinganalysis
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation

    cs.CL 2025-07 conditional novelty 7.0 of 10

    With prompt engineering (audio concatenation plus in-context examples), large audio models rank speech synthesis systems in line with human preferences, reaching up to 0.91 Spearman correlation.

  2. LLaSO: A Foundational Framework for Reproducible Research in Large Language and Speech Model

    cs.CL 2025-08 unverdicted novelty 6.0 of 10

    LLaSO releases a 3.8B speech-language model, 25.5M training instances, and an evaluation benchmark, claiming a normalized score of 0.72.

  3. Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs

    cs.CL 2025-08 unverdicted novelty 4.0 of 10

    Under matched settings, continuous SSL speech features generally outperform discrete tokens on six spoken language understanding tasks in SpeechLLMs.

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