mlsanityData to 7 Oct 2026

Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numbers

arXiv 2104.01604 ↗First posted 4 Apr 2021cs.CL eess.AScited by 11

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Abstract (arXiv)

This paper introduces Timers and Such, a new open source dataset of spoken English commands for common voice control use cases involving numbers. We describe the gap in existing spoken language understanding datasets that Timers and Such fills, the design and creation of the dataset, and experiments with a number of ASR-based and end-to-end baseline models, the code for which has been made available as part of the SpeechBrain toolkit.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 43 references that are in the map. First posted on arXiv 4 Apr 2021.

Builds on

24 of 43 references.

References · 24

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12 more
  1. 2018Towards end-to-end spoken language understandingcited by 92
  2. 2019Learning Problem-agnostic Speech Representations from Multiple Self-supervised Taskscited by 87
  3. 2020Rethinking Evaluation in ASR: Are Our Models Robust Enough?cited by 72
  4. 2018From Audio to Semantics: Approaches to end-to-end spoken language understandingcited by 63
  5. 2020Viewmaker Networks: Learning Views for Unsupervised Representation Learningcited by 48
  6. 2020Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systemscited by 38
  7. 2018Spoken Language Understanding on the Edgecited by 27
  8. 2019Using Speech Synthesis to Train End-to-End Spoken Language Understanding Modelscited by 24
  9. 2019Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learningcited by 12
  10. 2021Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classificationcited by 11
  11. 2020SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understandingcited by 5
  12. 2020A low latency ASR-free end to end spoken language understanding systemcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 0; 2024: 3; 2025: 2; 2026: 1.

11 papers.

  1. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 404
  2. 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
  3. 2022SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Taskscited by 34
  4. 2025Towards Spoken Mathematical Reasoning: Benchmarking Speech-based Models over Multi-faceted Math Problemscited by 12
  5. 2022Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audiocited by 6
  6. 2022Finstreder: Simple and fast Spoken Language Understanding with Finite State Transducers using modern Speech-to-Text modelscited by 2
  7. 2024MSNER: A Multilingual Speech Dataset for Named Entity Recognitioncited by 2
  8. 2024Digits micro-model for accurate and secure transactionscited by 0
  9. 2024Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language Understandingcited by 0
  10. 2025"KAN you hear me?" Exploring Kolmogorov-Arnold Networks for Spoken Language Understandingcited by 0
  11. 2026SLURP-TN : Resource for Tunisian Dialect Spoken Language Understandingcited by 0