# Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization

arXiv: https://arxiv.org/abs/2304.14535 · HTML: https://mlsanity.com/paper/2304.14535/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 85 of its 275 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 30 of 85)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Domain Separation Networks](https://mlsanity.com/paper/1608.06019/) (2016)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Characterizing and Avoiding Negative Transfer](https://mlsanity.com/paper/1811.09751/) (2018)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [A Survey on Negative Transfer](https://mlsanity.com/paper/2009.00909/) (2020)
- [Self-training and Pre-training are Complementary for Speech Recognition](https://mlsanity.com/paper/2010.11430/) (2020)
- [Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding](https://mlsanity.com/paper/1808.05665/) (2018)
- [An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation](https://mlsanity.com/paper/2008.09586/) (2020)
- [Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data](https://mlsanity.com/paper/2104.07778/) (2021)
- [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://mlsanity.com/paper/2109.06870/) (2021)
- [Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences](https://mlsanity.com/paper/1909.08582/) (2019)
- [A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks](https://mlsanity.com/paper/2010.11338/) (2020)
- [Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer](https://mlsanity.com/paper/1910.13923/) (2019)
- [Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1808.09347/) (2018)
- [Transfer Learning with Dynamic Distribution Adaptation](https://mlsanity.com/paper/1909.08531/) (2019)
- [Hear "No Evil", See "Kenansville": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems](https://mlsanity.com/paper/1910.05262/) (2019)
- [Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control](https://mlsanity.com/paper/2010.11037/) (2020)
- [Improved Fine-Tuning by Better Leveraging Pre-Training Data](https://mlsanity.com/paper/2111.12292/) (2021)
- [Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling](https://mlsanity.com/paper/1810.03459/) (2018)
- [A Survey on Transfer Learning in Natural Language Processing](https://mlsanity.com/paper/2007.04239/) (2020)
- [Efficient Adapter Transfer of Self-Supervised Speech Models for Automatic Speech Recognition](https://mlsanity.com/paper/2202.03218/) (2022)
- [Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT](https://mlsanity.com/paper/2102.07594/) (2021)

## Built on by (most cited first, 28 of 28)

- [Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey](https://mlsanity.com/paper/2403.01255/) (2024)
- [Federated Learning for Computer Vision](https://mlsanity.com/paper/2308.13558/) (2023)
- [Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey](https://mlsanity.com/paper/2408.07583/) (2024)
- [AI in Thyroid Cancer Diagnosis: Techniques, Trends, and Future Directions](https://mlsanity.com/paper/2308.13592/) (2023)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems](https://mlsanity.com/paper/2405.18624/) (2024)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Automatic Speech Recognition with BERT and CTC Transformers: A Review](https://mlsanity.com/paper/2410.09456/) (2024)
- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection](https://mlsanity.com/paper/2504.13186/) (2025)
- [Current Topological and Machine Learning Applications for Bias Detection in Text](https://mlsanity.com/paper/2311.13495/) (2023)
- [Federated and Transfer Learning for Cancer Detection Based on Image Analysis](https://mlsanity.com/paper/2405.20126/) (2024)
- [Deep Transfer Learning for Kidney Cancer Diagnosis](https://mlsanity.com/paper/2408.04318/) (2024)
- [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025)
- [Recent Advances in Transformer and Large Language Models for UAV Applications](https://mlsanity.com/paper/2508.11834/) (2025)
- [Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review](https://mlsanity.com/paper/2403.13843/) (2024)
- [Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review](https://mlsanity.com/paper/2409.07128/) (2024)
- [Advancing Arabic Speech Recognition Through Large-Scale Weakly Supervised Learning](https://mlsanity.com/paper/2504.12254/) (2025)
- [Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques](https://mlsanity.com/paper/2410.18972/) (2024)
- [Improving Pretrained YAMNet for Enhanced Speech Command Detection via Transfer Learning](https://mlsanity.com/paper/2504.19030/) (2025)
- [Enhancing Cochlear Implant Signal Coding with Scaled Dot-Product Attention](https://mlsanity.com/paper/2504.19046/) (2025)
- [From Dialect Gaps to Identity Maps: Tackling Variability in Speaker Verification](https://mlsanity.com/paper/2505.04629/) (2025)
- [Benchmarking Automatic Speech Recognition Models for African Languages](https://mlsanity.com/paper/2512.10968/) (2025)
- [SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments](https://mlsanity.com/paper/2604.06254/) (2026)
- [Transliterated Zero-Shot Domain Adaptation for Automatic Speech Recognition](https://mlsanity.com/paper/2412.11185/) (2024)
- [Hybrid Artificial Intelligence Strategies for Drone Navigation](https://mlsanity.com/paper/2501.04472/) (2025)
- [Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments](https://mlsanity.com/paper/2505.01632/) (2025)
- [Hybrid ResNet-1D-BiGRU with Multi-Head Attention for Cyberattack Detection in Industrial IoT Environments](https://mlsanity.com/paper/2604.06481/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) has recently become an important challenge when using deep learning (DL). It requires large-scale training datasets and high computational and storage resources. Moreover, DL techniques and machine learning (ML) approaches in general, hypothesize that training and testing data come from the same domain, with the same input feature space and data distribution characteristics. This assumption, however, is not applicable in some real-world artificial intelligence (AI) applications. Moreover, there are situations where gathering real data is challenging, expensive, or rarely occurring, which can not meet the data requirements of DL models. deep transfer learning (DTL) has been introduced to overcome these issues, which helps develop high-performing models using real datasets that are small or slightly different but related to the training data. This paper presents a comprehensive survey of DTL-based ASR frameworks to shed light on the latest developments and helps academics and professionals understand current challenges. Specifically, after presenting the DTL background, a well-designed taxonomy is adopted to inform the state-of-the-art. A critical analysis is then conducted to identify the limitations and advantages of each framework. Moving on, a comparative study is introduced to highlight the current challenges before deriving opportunities for future research.