# Automatic Speech Recognition with BERT and CTC Transformers: A Review

arXiv: https://arxiv.org/abs/2410.09456 · HTML: https://mlsanity.com/paper/2410.09456/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 52 references that are in the map.
- First posted on arXiv 12 Oct 2024.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict](https://mlsanity.com/paper/2005.08700/) (2020)
- [Relaxing the Conditional Independence Assumption of CTC-based ASR by Conditioning on Intermediate Predictions](https://mlsanity.com/paper/2104.02724/) (2021)
- [Context-Aware Transformer Transducer for Speech Recognition](https://mlsanity.com/paper/2111.03250/) (2021)
- [LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT](https://mlsanity.com/paper/2203.15610/) (2022)
- [Distilling the Knowledge of BERT for Sequence-to-Sequence ASR](https://mlsanity.com/paper/2008.03822/) (2020)
- [Transformers in Speech Processing: A Survey](https://mlsanity.com/paper/2303.11607/) (2023)
- [Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization](https://mlsanity.com/paper/2304.14535/) (2023)
- [Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input](https://mlsanity.com/paper/2010.15025/) (2020)
- [Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection](https://mlsanity.com/paper/2003.01309/) (2020)
- [Innovative Bert-based Reranking Language Models for Speech Recognition](https://mlsanity.com/paper/2104.04950/) (2021)
- [Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey](https://mlsanity.com/paper/2408.07583/) (2024)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification](https://mlsanity.com/paper/2108.02598/) (2021)
- [Speech Pre-training with Acoustic Piece](https://mlsanity.com/paper/2204.03240/) (2022)
- [N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses](https://mlsanity.com/paper/2106.06519/) (2021)
- [Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates](https://mlsanity.com/paper/2109.12804/) (2021)
- [Attention-based Multi-hypothesis Fusion for Speech Summarization](https://mlsanity.com/paper/2111.08201/) (2021)
- [An Improved Single Step Non-autoregressive Transformer for Automatic Speech Recognition](https://mlsanity.com/paper/2106.09885/) (2021)
- [Cross-utterance Reranking Models with BERT and Graph Convolutional Networks for Conversational Speech Recognition](https://mlsanity.com/paper/2106.06922/) (2021)
- [An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering](https://mlsanity.com/paper/2005.12142/) (2020)
- [Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset](https://mlsanity.com/paper/2006.08274/) (2020)
- [Discriminative Self-training for Punctuation Prediction](https://mlsanity.com/paper/2104.10339/) (2021)
- [Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System](https://mlsanity.com/paper/1910.01289/) (2019)
- [Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives](https://mlsanity.com/paper/2403.15442/) (2024)
- [An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features](https://mlsanity.com/paper/2006.01189/) (2020)
- [Toward Streaming ASR with Non-Autoregressive Insertion-based Model](https://mlsanity.com/paper/2012.10128/) (2020)

## Built on by (most cited first, 8 of 8)

- [Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models](https://mlsanity.com/paper/2501.13962/) (2025)
- [Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection](https://mlsanity.com/paper/2504.13186/) (2025)
- [Recent Advances in Transformer and Large Language Models for UAV Applications](https://mlsanity.com/paper/2508.11834/) (2025)
- [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)
- [Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments](https://mlsanity.com/paper/2505.01632/) (2025)
- [SP-MCQA: Evaluating Intelligibility of TTS Beyond the Word Level](https://mlsanity.com/paper/2510.26190/) (2025)
- [Overcoming Decoder Inconsistencies in Whisper for Dravidian and Low-Resource Languages](https://mlsanity.com/paper/2606.09535/) (2026)

## Abstract (arXiv)

This review paper provides a comprehensive analysis of recent advances in automatic speech recognition (ASR) with bidirectional encoder representations from transformers BERT and connectionist temporal classification (CTC) transformers. The paper first introduces the fundamental concepts of ASR and discusses the challenges associated with it. It then explains the architecture of BERT and CTC transformers and their potential applications in ASR. The paper reviews several studies that have used these models for speech recognition tasks and discusses the results obtained. Additionally, the paper highlights the limitations of these models and outlines potential areas for further research. All in all, this review provides valuable insights for researchers and practitioners who are interested in ASR with BERT and CTC transformers.