mlsanityData to 14 Oct 2026

Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces

arXiv 1805.10190 ↗First posted 25 May 2018cs.CL cs.NEcited by 377

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Abstract (arXiv)

This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices. The embedded inference is fast and accurate while enforcing privacy by design, as no personal user data is ever collected. Focusing on Automatic Speech Recognition and Natural Language Understanding, we detail our approach to training high-performance Machine Learning models that are small enough to run in real-time on small devices. Additionally, we describe a data generation procedure that provides sufficient, high-quality training data without compromising user privacy.

Key facts. Cited by 377 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 May 2018.

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Citing papers per year: 2021: 90; 2022: 85; 2023: 84; 2024: 67; 2025: 29; 2026: 22.

377 papers.

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  1. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 666
  2. 2023Instruction Tuning for Large Language Models: A Surveycited by 623
  3. 2022MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languagescited by 146
  4. 2021Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue Systemcited by 139
  5. 2024AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agentscited by 107
  6. 2021Language Models are Few-shot Multilingual Learnerscited by 92
  7. 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
  8. 2021SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speechcited by 72
  9. 2021Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimationcited by 71
  10. 2024Self-Supervised Speech Representations are More Phonetic than Semanticcited by 71
  11. 2024LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancementcited by 70
  12. 2021BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modelingcited by 56
38 more
  1. 2021Neural Data Augmentation via Example Extrapolationcited by 54
  2. 2021A Survey on Spoken Language Understanding: Recent Advances and New Frontierscited by 53
  3. 2021ESPnet-SLU: Advancing Spoken Language Understanding through ESPnetcited by 53
  4. 2022Data Augmentation for Intent Classification with Off-the-shelf Large Language Modelscited by 49
  5. 2023FinGPT: Large Generative Models for a Small Languagecited by 49
  6. 2021Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuningcited by 46
  7. 2024Recent Advances of Foundation Language Models-based Continual Learning: A Surveycited by 46
  8. 2022DeepStruct: Pretraining of Language Models for Structure Predictioncited by 45
  9. 2021Visual Goal-Step Inference using wikiHowcited by 43
  10. 2021From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understandingcited by 43
  11. 2022MIntRec: A New Dataset for Multimodal Intent Recognitioncited by 41
  12. 2023ChatGPT to Replace Crowdsourcing of Paraphrases for Intent Classification: Higher Diversity and Comparable Model Robustnesscited by 41
  13. 2022When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computingcited by 40
  14. 2024A Large-Scale Evaluation of Speech Foundation Modelscited by 40
  15. 2022InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuningcited by 39
  16. 2024API-BLEND: A Comprehensive Corpora for Training and Benchmarking API LLMscited by 39
  17. 2021TWEAC: Transformer with Extendable QA Agent Classifierscited by 37
  18. 2023Making LLMs Worth Every Penny: Resource-Limited Text Classification in Bankingcited by 37
  19. 2021Few-shot Intent Classification and Slot Filling with Retrieved Examplescited by 35
  20. 2021Multilingual and Cross-Lingual Intent Detection from Spoken Datacited by 35
  21. 2022SLUE Phase-2: A Benchmark Suite of Diverse Spoken Language Understanding Taskscited by 35
  22. 2021Open-world Machine Learning: Applications, Challenges, and Opportunitiescited by 34
  23. 2022SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generationcited by 34
  24. 2026Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Datacited by 34
  25. 2021GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Fillingcited by 33
  26. 2023ZeroShotDataAug: Generating and Augmenting Training Data with ChatGPTcited by 32
  27. 2021ADePT: Auto-encoder based Differentially Private Text Transformationcited by 31
  28. 2023A Preliminary Evaluation of ChatGPT for Zero-shot Dialogue Understandingcited by 31
  29. 2024A Survey on Data Augmentation in Large Model Eracited by 31
  30. 2024EmoLLM: Multimodal Emotional Understanding Meets Large Language Modelscited by 31
  31. 2024AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Modelscited by 30
  32. 2024Efficacy of Synthetic Data as a Benchmarkcited by 30
  33. 2022LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Taggingcited by 29
  34. 2022DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewritingcited by 28
  35. 2022SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understandingcited by 28
  36. 2023DP-BART for Privatized Text Rewriting under Local Differential Privacycited by 28
  37. 2021An Efficient DP-SGD Mechanism for Large Scale NLP Modelscited by 27
  38. 2021An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labelingcited by 26

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