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Vers la compr'ehension automatique de la parole bout-en-bout `a moindre effort

arXiv 2207.00349 ↗First posted 1 Jul 2022cs.CL cs.AIcited by 0

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Abstract (arXiv)

Recent advances in spoken language understanding benefited from Self-Supervised models trained on large speech corpora. For French, the LeBenchmark project has made such models available and has led to impressive progress on several tasks including spoken language understanding. These advances have a non-negligible cost in terms of computation time and energy consumption. In this paper, we compare several learning strategies aiming at reducing such cost while keeping competitive performances. The experiments are performed on the MEDIA corpus, and show that it is possible to reduce the learning cost while maintaining state-of-the-art performances.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 41 references that are in the map. First posted on arXiv 1 Jul 2022.

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References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  4. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  5. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 652
  6. 2019Speech Model Pre-training for End-to-End Spoken Language Understandingcited by 191
  7. 2018Towards end-to-end spoken language understandingcited by 92
  8. 2021LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speechcited by 46
  9. 2020Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretrainingcited by 35
  10. 2020End-to-End Neural Transformer Based Spoken Language Understandingcited by 31
  11. 2019Curriculum-based transfer learning for an effective end-to-end spoken language understanding and domain portabilitycited by 29
  12. 2021Where are we in semantic concept extraction for Spoken Language Understanding?cited by 11
4 more
  1. 2021End2End Acoustic to Semantic Transductioncited by 10
  2. 2020A Data Efficient End-To-End Spoken Language Understanding Architecturecited by 8
  3. 2016Improving Recurrent Neural Networks For Sequence Labellingcited by 3
  4. 2017Label-Dependencies Aware Recurrent Neural Networkscited by 3

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