mlsanityData to 14 Oct 2026

Exploration of Adapter for Noise Robust Automatic Speech Recognition

arXiv 2402.18275 ↗First posted 28 Feb 2024cs.SD cs.CL eess.AScited by 4

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Abstract (arXiv)

Adapting an automatic speech recognition (ASR) system to unseen noise environments is crucial. Integrating adapters into neural networks has emerged as a potent technique for transfer learning. This study thoroughly investigates adapter-based ASR adaptation in noisy environments. We conducted experiments using the CHiME--4 dataset. The results show that inserting the adapter in the shallow layer yields superior effectiveness, and there is no significant difference between adapting solely within the shallow layer and adapting across all layers. The simulated data helps the system to improve its performance under real noise conditions. Nonetheless, when the amount of data is the same, the real data is more effective than the simulated data. Multi-condition training is still useful for adapter training. Furthermore, integrating adapters into speech enhancement-based ASR systems yields substantial improvements.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 28 Feb 2024.

Builds on

18 of 29 references.

References · 18

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  4. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  5. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
  6. 2017Learning multiple visual domains with residual adapterscited by 557
  7. 2015MUSAN: A Music, Speech, and Noise Corpuscited by 425
  8. 2020Real Time Speech Enhancement in the Waveform Domaincited by 186
  9. 2022Contextual Adapters for Personalized Speech Recognition in Neural Transducerscited by 57
  10. 2018Domain Adversarial Training for Accented Speech Recognitioncited by 40
  11. 2022Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognitioncited by 32
  12. 2023From English to More Languages: Parameter-Efficient Model Reprogramming for Cross-Lingual Speech Recognitioncited by 23
6 more
  1. 2023Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRcited by 23
  2. 2020ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integrationcited by 22
  3. 2019What does a network layer hear? Analyzing hidden representations of end-to-end ASR through speech synthesiscited by 13
  4. 2022Layer-wise Fast Adaptation for End-to-End Multi-Accent Speech Recognitioncited by 12
  5. 2023Gradient Remedy for Multi-Task Learning in End-to-End Noise-Robust Speech Recognitioncited by 10
  6. 2023Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learningcited by 9

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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Serialized Speech Information Guidance with Overlapped Encoding Separation for Multi-Speaker Automatic Speech Recognitioncited by 7
  2. 2024Adapting Whisper for Code-Switching through Encoding Refining and Language-Aware Decodingcited by 7
  3. 2025Serialized Output Prompting for Large Language Model-based Multi-Talker Speech Recognitioncited by 5
  4. 2024Error Correction by Paying Attention to Both Acoustic and Confidence References for Automatic Speech Recognitioncited by 2