mlsanityData to 14 Oct 2026

Towards end-to-end spoken language understanding

arXiv 1802.08395 ↗First posted 23 Feb 2018cs.CLcited by 92

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Abstract (arXiv)

Spoken language understanding system is traditionally designed as a pipeline of a number of components. First, the audio signal is processed by an automatic speech recognizer for transcription or n-best hypotheses. With the recognition results, a natural language understanding system classifies the text to structured data as domain, intent and slots for down-streaming consumers, such as dialog system, hands-free applications. These components are usually developed and optimized independently. In this paper, we present our study on an end-to-end learning system for spoken language understanding. With this unified approach, we can infer the semantic meaning directly from audio features without the intermediate text representation. This study showed that the trained model can achieve reasonable good result and demonstrated that the model can capture the semantic attention directly from the audio features.

Key facts. Cited by 92 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Feb 2018.

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Citing papers per year: 2021: 31; 2022: 23; 2023: 19; 2024: 11; 2025: 4; 2026: 4.

92 papers.

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  1. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 406
  2. 2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
  3. 2022One Country, 700+ Languages: NLP Challenges for Underrepresented Languages and Dialects in Indonesiacited by 66
  4. 2021Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understandingcited by 42
  5. 2021Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answeringcited by 39
  6. 2022End-to-end Spoken Conversational Question Answering: Task, Dataset and Modelcited by 37
  7. 20244D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic Scenescited by 37
  8. 2021Self-supervised Dialogue Learning for Spoken Conversational Question Answeringcited by 28
  9. 2021Speech-language Pre-training for End-to-end Spoken Language Understandingcited by 27
  10. 2021Integration of Pre-trained Networks with Continuous Token Interface for End-to-End Spoken Language Understandingcited by 22
  11. 2022STOP: A dataset for Spoken Task Oriented Semantic Parsingcited by 22
  12. 2021Phoneme-BERT: Joint Language Modelling of Phoneme Sequence and ASR Transcriptcited by 21
38 more
  1. 2023Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understandingcited by 21
  2. 2022Towards Reducing the Need for Speech Training Data To Build Spoken Language Understanding Systemscited by 20
  3. 2022A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understandingcited by 18
  4. 2023LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speechcited by 18
  5. 2021Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systemscited by 15
  6. 2021Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classificationcited by 14
  7. 2023LibriSQA: A Novel Dataset and Framework for Spoken Question Answering with Large Language Modelscited by 14
  8. 2024Speech Retrieval-Augmented Generation without Automatic Speech Recognitioncited by 14
  9. 2021Leveraging Acoustic and Linguistic Embeddings from Pretrained speech and language Models for Intent Classificationcited by 11
  10. 2021Timers and Such: A Practical Benchmark for Spoken Language Understanding with Numberscited by 11
  11. 2021Where are we in semantic concept extraction for Spoken Language Understanding?cited by 11
  12. 2022End-to-end model for named entity recognition from speech without paired training datacited by 11
  13. 2021End2End Acoustic to Semantic Transductioncited by 10
  14. 2022Towards Relation Extraction From Speechcited by 10
  15. 2021Do as I mean, not as I say: Sequence Loss Training for Spoken Language Understandingcited by 9
  16. 2021Integrating Dialog History into End-to-End Spoken Language Understanding Systemscited by 9
  17. 2021ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language Understandingcited by 9
  18. 2021Speak or Chat with Me: End-to-End Spoken Language Understanding System with Flexible Inputscited by 8
  19. 2021Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-Endcited by 8
  20. 2021FANS: Fusing ASR and NLU for on-device SLUcited by 8
  21. 2022Deliberation Model for On-Device Spoken Language Understandingcited by 8
  22. 2021RNN Transducer Models For Spoken Language Understandingcited by 7
  23. 2021Sequential End-to-End Intent and Slot Label Classification and Localizationcited by 7
  24. 2022On the Use of Semantically-Aligned Speech Representations for Spoken Language Understandingcited by 7
  25. 2021Pre-training for Spoken Language Understanding with Joint Textual and Phonetic Representation Learningcited by 6
  26. 2021Speech2Slot: An End-to-End Knowledge-based Slot Filling from Speechcited by 6
  27. 2022Bottleneck Low-rank Transformers for Low-resource Spoken Language Understandingcited by 6
  28. 2022ASR-Generated Text for Language Model Pre-training Applied to Speech Taskscited by 6
  29. 2022End-to-end Spoken Language Understanding with Tree-constrained Pointer Generatorcited by 6
  30. 2023Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Targetcited by 6
  31. 2024Using Large Language Model for End-to-End Chinese ASR and NERcited by 6
  32. 2021Representation based meta-learning for few-shot spoken intent recognitioncited by 5
  33. 2021Learning Visual-Audio Representations for Voice-Controlled Robotscited by 5
  34. 2023OLISIA: a Cascade System for Spoken Dialogue State Trackingcited by 5
  35. 2023Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Trainingcited by 5
  36. 2021Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systemscited by 4
  37. 2022On Building Spoken Language Understanding Systems for Low Resourced Languagescited by 4
  38. 2023The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translationcited by 4

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