mlsanityData to 7 Oct 2026

Injecting Text and Cross-lingual Supervision in Few-shot Learning from Self-Supervised Models

arXiv 2110.04863 ↗First posted 10 Oct 2021eess.AS cs.CLcited by 2

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Abstract (arXiv)

Self-supervised model pre-training has recently garnered significant interest, but relatively few efforts have explored using additional resources in fine-tuning these models. We demonstrate how universal phoneset acoustic models can leverage cross-lingual supervision to improve transfer of pretrained self-supervised representations to new languages. We also show how target-language text can be used to enable and improve fine-tuning with the lattice-free maximum mutual information (LF-MMI) objective. In three low-resource languages these techniques greatly improved few-shot learning performance.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 25 of its 47 references that are in the map. First posted on arXiv 10 Oct 2021.

Builds on

25 of 47 references.

References · 25

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2017Neural Discrete Representation Learningcited by 4,650
  4. 2013Auto-Encoding Variational Bayescited by 3,980
  5. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  6. 2016Wide Residual Networkscited by 3,057
  7. 2019On Variational Bounds of Mutual Informationcited by 550
  8. 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 496
  9. 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 462
  10. 2021VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretationcited by 455
  11. 2019vq-wav2vec: Self-Supervised Learning of Discrete Speech Representationscited by 335
  12. 2018TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptationcited by 224
13 more
  1. 2019An Unsupervised Autoregressive Model for Speech Representation Learningcited by 185
  2. 2021Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Trainingcited by 148
  3. 2019Self-Training for End-to-End Speech Recognitioncited by 128
  4. 2020Unsupervised pretraining transfers well across languagescited by 100
  5. 2020Exploring wav2vec 2.0 on speaker verification and language identificationcited by 93
  6. 2021Simple and Effective Zero-shot Cross-lingual Phoneme Recognitioncited by 79
  7. 2019Deep Contextualized Acoustic Representations For Semi-Supervised Speech Recognitioncited by 66
  8. 2020Learning Robust and Multilingual Speech Representationscited by 39
  9. 2021Injecting Text in Self-Supervised Speech Pretrainingcited by 24
  10. 2018Sequence-based Multi-lingual Low Resource Speech Recognitioncited by 20
  11. 2020PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASRcited by 16
  12. 2018Adversarial Training of End-to-end Speech Recognition Using a Criticizing Language Modelcited by 11
  13. 2020Lattice-Free MMI Adaptation Of Self-Supervised Pretrained Acoustic Modelscited by 5

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Citing papers per year: 2022: 2.

2 papers.

  1. 2022Effective Transfer Learning for Low-Resource Natural Language Understandingcited by 1
  2. 2022Training Autoregressive Speech Recognition Models with Limited in-domain Supervisioncited by 0