Data to 7 Oct 2026

Attention-Based Models for Speech Recognition

arXiv 1506.07503 ↗First posted 24 Jun 2015cs.CL cs.LG cs.NEcited by 400

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Abstract (arXiv)

Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks in- cluding machine translation, handwriting synthesis and image caption gen- eration. We extend the attention-mechanism with features needed for speech recognition. We show that while an adaptation of the model used for machine translation in reaches a competitive 18.7% phoneme error rate (PER) on the TIMIT phoneme recognition task, it can only be applied to utterances which are roughly as long as the ones it was trained on. We offer a qualitative explanation of this failure and propose a novel and generic method of adding location-awareness to the attention mechanism to alleviate this issue. The new method yields a model that is robust to long inputs and achieves 18% PER in single utterances and 20% in 10-times longer (repeated) utterances. Finally, we propose a change to the at- tention mechanism that prevents it from concentrating too much on single frames, which further reduces PER to 17.6% level.

Key facts. Cited by 400 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 119; 2022: 107; 2023: 84; 2024: 51; 2025: 28; 2026: 11.

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2021Embracing Single Stride 3D Object Detector with Sparse Transformercited by 193
2021WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognitioncited by 189
2021A Survey on Neural Speech Synthesiscited by 182
2021Unsupervised Speech Recognitioncited by 147
2023Neural Architecture Search: Insights from 1000 Paperscited by 136
2023End-to-End Speech Recognition: A Surveycited by 127
2021Transformer-Based Attention Networks for Continuous Pixel-Wise Predictioncited by 125
2021WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkitcited by 123
2021Intermediate Loss Regularization for CTC-based Speech Recognitioncited by 95
2022Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methodscited by 75
2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
2022BiBERT: Accurate Fully Binarized BERTcited by 70

38 more

2022A General Survey on Attention Mechanisms in Deep Learningcited by 64
2022ScePT: Scene-consistent, Policy-based Trajectory Predictions for Planningcited by 63
2022Contextual Adapters for Personalized Speech Recognition in Neural Transducerscited by 57
2021Context-Aware Transformer Transducer for Speech Recognitioncited by 56
2023Efficient Sequence Transduction by Jointly Predicting Tokens and Durationscited by 56
2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
2024Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Reviewcited by 50
2023Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognitioncited by 48
2021One TTS Alignment To Rule Them Allcited by 47
2022WeNet 2.0: More Productive End-to-End Speech Recognition Toolkitcited by 46
2023Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Studycited by 42
2022Lip to Speech Synthesis with Visual Context Attentional GANcited by 41
2021Neural Dubber: Dubbing for Videos According to Scriptscited by 39
2022Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selectioncited by 39
2022An Extensive Data Processing Pipeline for MIMIC-IVcited by 39
2021Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognitioncited by 37
2021Momentum Pseudo-Labeling for Semi-Supervised Speech Recognitioncited by 37
2022Streaming Multi-Talker ASR with Token-Level Serialized Output Trainingcited by 37
2022Visual Attention Methods in Deep Learning: An In-Depth Surveycited by 37
2021Large-Scale Self- and Semi-Supervised Learning for Speech Translationcited by 36
2021Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphonecited by 33
2021Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognitioncited by 32
2021Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERTcited by 31
2021Factorized Neural Transducer for Efficient Language Model Adaptationcited by 31
2023MoLE : Mixture of Language Experts for Multi-Lingual Automatic Speech Recognitioncited by 31
2023Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Networkcited by 31
2021On the limit of English conversational speech recognitioncited by 30
2024HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Imagescited by 30
2021Transformer Based Deliberation for Two-Pass Speech Recognitioncited by 29
2022Attention Mechanism in Neural Networks: Where it Comes and Where it Goescited by 29
2021U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognitioncited by 28
2021Speech-language Pre-training for End-to-end Spoken Language Understandingcited by 27
2021A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generationcited by 27
2022Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languagescited by 26
2023Language-Routing Mixture of Experts for Multilingual and Code-Switching Speech Recognitioncited by 26
2023A Machine Learning-oriented Survey on Tiny Machine Learningcited by 26
2022CTC Alignments Improve Autoregressive Translationcited by 25
2021Pseudo-Labeling for Massively Multilingual Speech Recognitioncited by 23

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