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SLURP: A Spoken Language Understanding Resource Package

Summary

The paper addresses spoken language understanding (SLU), that is, extracting the meaning of a voice command directly from audio. End-to-end (E2E) systems lack large and diverse corpora in which speech recordings carry structured semantic annotation. The authors present SLURP, an English-language package. Its core is about 72k recordings of commands to a home assistant, annotated at three levels: scenario, action and entities. First, AMT workers composed the command texts from prompts that were deliberately designed not to suggest specific words. Then more than a hundred participants read these texts aloud in a room approximating household conditions, with recording done by a far-field microphone array and a headset. Besides the data, the package includes pipeline baseline systems (ASR + NLU) and a new metric, SLU-F1, for entity evaluation. It gives credit for partially correct entity values and yields an error distribution suitable for analysis. The modular pipelines proved to be a strong baseline. By contrast, well-known SOTA E2E models failed to learn on these data. Thus, SLURP poses a considerably harder and lexically richer task for the field than previous corpora.

Key points

  • Size: 72,277 audio files, of which 34,603 were recorded with a headset and 37,674 with a far-field microphone. This amounts to 177 speakers, approximately 58 hours of audio and 17,181 sentences covering 18 scenarios, 46 actions and 55 entity types. There is also a separate version, SLURP-synth: 69,253 synthesized recordings voiced by 34 Google TTS voices.
  • Comparison with Snips and FSC: there are about 6 times more sentences than in Snips, and about 2.5 times more audio than in FSC. There are 9 times more domains, and the corpus is on average about 10 times lexically richer. SLURP also outperforms both corpora on the LS2, CVS1 and MSTTR measures. It falls behind only in average sentence length.
  • Quality control: recordings that could not be force-aligned with their text in Multi-ASR were removed. For the rest, the Multi-ASR and Google-ASR transcripts were compared with the prompt texts. For approximately 60% of the data, at least one of these systems achieved WER=0. Adding recordings in which all entities were recognized correctly (EntityWER=0) raises the share to approximately 73%.
  • Baselines: combinations of two NLU and two ASR systems were compared; HerMiT/Multi-ASR performed best. For entities, top-down decoding works better: first the scenario and action, then the entities. SLU-F1 correlates weakly with standard F1 (ρ = 0.2), although it ranks the systems in the same order.
  • E2E models: SOTA E2E models, including the model of Lugosch et al. (2019b), did not converge after retraining on SLURP. The resource is published on GitHub: pswietojanski/slurp.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cited by

86 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 85 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2020.emnlp-main.588 · arXiv 2011.13205

@inproceedings{bastianelli2020slurp,
  title = {{SLURP: A Spoken Language Understanding Resource Package}},
  author = {Bastianelli, Emanuele and Vanzo, Andrea and Swietojanski, Pawel and Rieser, Verena},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.588},
  eprint = {2011.13205},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.588}
}

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