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Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM

arXiv 1706.02737 ↗First posted 8 Jun 2017cs.CLcited by 43

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Abstract (arXiv)

We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC network sits on top of the encoder and is jointly trained with the attention-based decoder. During the beam search process, we combine the CTC predictions, the attention-based decoder predictions and a separately trained LSTM language model. We achieve a 5-10% error reduction compared to prior systems on spontaneous Japanese and Chinese speech, and our end-to-end model beats out traditional hybrid ASR systems.

Key facts. Cited by 43 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jun 2017.

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Citing papers per year: 2021: 19; 2022: 6; 2023: 7; 2024: 4; 2025: 5; 2026: 2.

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  1. 2021Recent Advances in End-to-End Automatic Speech Recognitioncited by 138
  2. 2023End-to-End Speech Recognition: A Surveycited by 127
  3. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
  4. 2023Language-Routing Mixture of Experts for Multilingual and Code-Switching Speech Recognitioncited by 26
  5. 2021Advanced Long-context End-to-end Speech Recognition Using Context-expanded Transformerscited by 25
  6. 2021Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Taskscited by 24
  7. 2022FitHuBERT: Going Thinner and Deeper for Knowledge Distillation of Speech Self-Supervised Learningcited by 24
  8. 2021Investigations on Speech Recognition Systems for Low-Resource Dialectal Arabic-English Code-Switching Speechcited by 15
  9. 2021Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillationcited by 12
  10. 2021SubSpectral Normalization for Neural Audio Data Processingcited by 11
  11. 2021WNARS: WFST based Non-autoregressive Streaming End-to-End Speech Recognitioncited by 10
  12. 2024Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Modelscited by 9
31 more
  1. 2024Contextualized Automatic Speech Recognition with Dynamic Vocabularycited by 8
  2. 2022On Compressing Sequences for Self-Supervised Speech Modelscited by 7
  3. 2023Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognitioncited by 7
  4. 2021A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognitioncited by 6
  5. 2021Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognitioncited by 5
  6. 2021Greenformers: Improving Computation and Memory Efficiency in Transformer Models via Low-Rank Approximationcited by 5
  7. 2022Joint Speech Recognition and Audio Captioningcited by 5
  8. 20223M: Multi-loss, Multi-path and Multi-level Neural Networks for speech recognitioncited by 5
  9. 2025Delayed Fusion: Integrating Large Language Models into First-Pass Decoding in End-to-end Speech Recognitioncited by 5
  10. 2021Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognitioncited by 4
  11. 2021Sequence Transduction with Graph-based Supervisioncited by 4
  12. 2024Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correctioncited by 4
  13. 2021Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillationcited by 3
  14. 2021Pre-training for low resource speech-to-intent applicationscited by 2
  15. 2021Capturing Multi-Resolution Context by Dilated Self-Attentioncited by 2
  16. 2021Modality Fusion Network and Personalized Attention in Momentary Stress Detection in the Wildcited by 2
  17. 2021Decoupling Visual-Semantic Feature Learning for Robust Scene Text Recognitioncited by 2
  18. 2022On Comparison of Encoders for Attention based End to End Speech Recognition in Standalone and Rescoring Modecited by 2
  19. 2023BLSTM-Based Confidence Estimation for End-to-End Speech Recognitioncited by 2
  20. 2024The Conformer Encoder May Reverse the Time Dimensioncited by 2
  21. 2021Two-Stage Augmentation and Adaptive CTC Fusion for Improved Robustness of Multi-Stream End-to-End ASRcited by 1
  22. 2023Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encodercited by 1
  23. 2023Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognitioncited by 1
  24. 2025A Study on Regularization-Based Continual Learning Methods for Indic ASRcited by 1
  25. 2026ViSpeechFormer: A Phonemic Approach for Vietnamese Automatic Speech Recognitioncited by 1
  26. 2022Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognitioncited by 0
  27. 2023BayesSpeech: A Bayesian Transformer Network for Automatic Speech Recognitioncited by 0
  28. 2025Audio-3DVG: Unified Audio -- Point Cloud Fusion for 3D Visual Groundingcited by 0
  29. 2025DESign: Dynamic Context-Aware Convolution and Efficient Subnet Regularization for Continuous Sign Language Recognitioncited by 0
  30. 2025Unified Learnable 2D Convolutional Feature Extraction for ASRcited by 0
  31. 2026WhisperPipe: A Resource-Efficient Streaming Architecture for Real-Time Automatic Speech Recognitioncited by 0