mlsanityData to 7 Oct 2026

Speech Recognition with no speech or with noisy speech

arXiv 1903.00739 ↗First posted 2 Mar 2019cs.LG stat.MLcited by 9

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Abstract (arXiv)

The performance of automatic speech recognition systems(ASR) degrades in the presence of noisy speech. This paper demonstrates that using electroencephalography (EEG) can help automatic speech recognition systems overcome performance loss in the presence of noise. The paper also shows that distillation training of automatic speech recognition systems using EEG features will increase their performance. Finally, we demonstrate the ability to recognize words from EEG with no speech signal on a limited English vocabulary with high accuracy.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2019.

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Citing papers per year: 2023: 1; 2024: 4; 2025: 4.

9 papers.

  1. 2024Large Language Models are Efficient Learners of Noise-Robust Speech Recognitioncited by 30
  2. 2024Model Inversion Robustness: Can Transfer Learning Help?cited by 11
  3. 2023Wav2code: Restore Clean Speech Representations via Codebook Lookup for Noise-Robust ASRcited by 10
  4. 2024On the Vulnerability of Skip Connections to Model Inversion Attackscited by 8
  5. 2025Neural Spelling: A Spell-Based BCI System for Language Neural Decodingcited by 4
  6. 2024LipGER: Visually-Conditioned Generative Error Correction for Robust Automatic Speech Recognitioncited by 1
  7. 2025Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessmentcited by 1
  8. 2025Acoustic to Articulatory Inversion of Speech; Data Driven Approaches, Challenges, Applications, and Future Scopecited by 0
  9. 2025Tracking Articulatory Dynamics in Speech with a Fixed-Weight BiLSTM-CNN Architecturecited by 0