The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Conformer: Convolution-augmented Transformer for Speech Recognition
Open accessAbstract
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. In this work, we achieve the best of both worlds by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. To this regard, we propose the convolution-augmented transformer for speech recognition, named Conformer. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In particular, transformer1 and recurrent neural-network transducer (RNNT)12,13,16 models containing up to one billion parameters2 have produced a marked decrease in word error rate (WER) (and therefore much better accuracy) for the automated transcription of spoken English-language sentences, as shown in Fig. 1a for two widely used datasets, Librispeech17 and SwitchBoard18.”
From An analog-AI chip for energy-efficient speech recognition and transcription (2023) · cites this paper as [16]
In the citing paper
“A range of acoustic models that give state-of-the-art performance on these tasks are chosen as the baseline speech recognition systems, including hybrid DNN [41], hybrid lattice-free maximum mutual information (LF-MMI) trained time delay neural network (TDNN) [3] and end-to-end (E2E) Conformer [6] models.”
From Speaker adaptation using spectro-temporal deep features for dysarthric and elderly speech recognition (2022) · cites this paper as [6]
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Self-citationAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Squeeze-and-Excitation NetworksPreprint
Hu, J., Shen, L., & Sun, G. (2017) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.48550/arxiv.1709.01507 · Cited by 37,845
(2014) · Cited by 33,419
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
“Librispeech: an ASR corpus based on public domain audio books,”
Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-2680 · Cited by 3,558
Self-citationTransformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169
Searching for activation functions
Ramachandran, P., Zoph, B., & Le, Q. V. (2017) · arXiv · doi:10.48550/arxiv.1710.05941 · Cited by 2,335
Convolutional Neural Networks for Speech Recognition
Abdel‐Hamid, O., Mohamed, A., Jiang, H., Deng, L., Penn, G., & Yu, D. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2339736 · Cited by 2,325
Flagged first, then by citations
Cited by
Sorted by citations
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Hsu, W.-N., Bolte, B., Tsai, Y.-H. H., Lakhotia, K., Salakhutdinov, R., & Mohamed, A. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3122291 · Cited by 2,983
CvT: Introducing Convolutions to Vision Transformers
Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., & Zhang, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00009 · Cited by 2,795
WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
Chen, S., Wang, C., Chen, Z., Wu, Y., Liu, S., Chen, Z., Li, J., Kanda, N., Yoshioka, T., Xiao, X., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3188113 · Cited by 1,926
Self-citationLin, T., Wang, Y., Liu, X., & Qiu, X. (2022) · AI Open · doi:10.1016/j.aiopen.2022.10.001 · Cited by 1,422
AST: Audio Spectrogram Transformer
Gong, Y., Chung, Y.-A., & Glass, J. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-698 · Cited by 1,159
Rethinking Attention with Performers
Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.14794 · Cited by 695
“Several solutions have been proposed to address this issue (Beltagy et al., 2020; Gulati et al., 2020; Chan et al., 2020; Child et al., 2019; Bello et al., 2019).”
BackgroundEgo4D: Around the World in 3,000 Hours of Egocentric Video
Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01842 · Cited by 630
Ding, Y., Jia, M., Miao, Q., & Cao, Y. (2022) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.108616 · Cited by 559
A comprehensive survey on applications of transformers for deep learning tasks
Islam, S., Elmekki, H., Elsebai, A., Bentahar, J., Drawel, N., Rjoub, G., & Witold Pedrycz, C. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122666 · Cited by 590
Audiolm: A language modeling approach to audio generation
Borsos, Z., Marinier, R., Vincent, D., Kharitonov, E., Pietquin, O., Sharifi, M., Roblek, D., Teboul, O., Grangier, D., Tagliasacchi, M., et al. (2023) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2023.3288409 · Cited by 432
Recent Advances in End-to-End Automatic Speech Recognition
Li, J. (2022) · APSIPA Transactions on Signal and Information Processing · doi:10.1561/116.00000050 · Cited by 451
Emotion Recognition from Speech Using wav2vec 2.0 Embeddings
Pepino, L., Riera, P., & Ferrer, L. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-703 · Cited by 389
A review of deep learning techniques for speech processing
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101869 · Cited by 389
Deep learning in food category recognition
Zhang, Y., Deng, L., Zhu, H., Wang, W., Ren, Z., Zhou, Q., Lu, S., Sun, S., Zhu, Z., Górriz, J. M., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101859 · Cited by 399
Self-citationA survey of sound source localization with deep learning methods
Grumiaux, P.-A., Kitić, S., Laurent Girin, B., & Guérin, A. (2022) · The Journal of the Acoustical Society of America · doi:10.1121/10.0011809 · Cited by 387
MetaFormer Baselines for Vision
Yu, W., Si, C., Zhou, P., Luo, M., Zhou, Y., Feng, J., Yan, S., & Wang, X. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3329173 · Cited by 401
Lin, K., Wang, L., & Liu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01270 · Cited by 361
Deep Learning Enabled Semantic Communications With Speech Recognition and Synthesis
Weng, Z., Qin, Z., Tao, X., Pan, C., Liu, G., & Li, G. Y. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3240969 · Cited by 310
Chung, Y.-A., Zhang, Y., Han, W., Chiu, C.-C., Qin, J., Pang, R., & Wu, Y. (2021) · 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · doi:10.1109/asru51503.2021.9688253 · Cited by 293
Self-citationWayformer: Motion Forecasting via Simple & Efficient Attention Networks
Nayakanti, N., Al‐Rfou, R., Zhou, A., Goel, K., Refaat, K. S., & Sapp, B. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160609 · Cited by 351
An analog-AI chip for energy-efficient speech recognition and transcription
Ambrogio, S., Narayanan, P., Okazaki, A., Fasoli, A., Mackin, C., Hosokawa, K., Nomura, A., Yasuda, T., Chen, A., Friz, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06337-5 · Cited by 269
“In particular, transformer 1 and recurrent neural-network transducer (RNNT) 12,13,16 models containing up to one billion parameters 2 have produced a marked decrease in word error rate (WER) (and therefore much better accuracy) for the automated transcription of spoken English-language sentences,…”
BackgroundEnd-To-End Audio-Visual Speech Recognition with Conformers
Ma, P., Petridis, S., & Pantić, M. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414567 · Cited by 240
WeNet: Production Oriented Streaming and Non-Streaming End-to-End Speech Recognition Toolkit
Yao, Z., Wu, D., Wang, X., Zhang, B., Yu, F., Yang, C., Peng, Z., Chen, X., Xie, L., & Lei, X. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1983 · Cited by 236
GigaSpeech: An Evolving, Multi-Domain ASR Corpus with 10,000 Hours of Transcribed Audio
Chen, G., Chai, S., Wang, G., Du, J., Zhang, W.-Q., Weng, C., Su, D., Povey, D., Trmal, J., Zhang, J., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1965 · Cited by 234
End-to-End Speech Recognition: A Survey
Prabhavalkar, R., Hori, T., Sainath, T. N., Schlüter, R., & Watanabe, S. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3328283 · Cited by 298
Han, S.-Y., Shao, H., Cheng, J., Yang, X., & Cai, B. (2022) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2022.3199985 · Cited by 224
When LLMs meet cybersecurity: a systematic literature review
Zhang, J., Bu, H., Wen, H., Liu, Y., Fei, H., Xi, R., Li, L., Yang, Y., Zhu, H., & Meng, D. (2025) · Cybersecurity · doi:10.1186/s42400-025-00361-w · Cited by 383
“It compares the original code with LLM-generated code to flag notable differences as anomalies for further inspection.”
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Cui, H., Dai, W., Zhu, Y., Kan, X., Gu, A. A. C., Lukemire, J., Zhan, L., He, L., Guo, Y., & Yang, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218745 · Cited by 218
FLEURS: FEW-Shot Learning Evaluation of Universal Representations of Speech
Conneau, A., Ma, M., Khanuja, S., Zhang, Y., Axelrod, V., Dalmia, S., Riesa, J., Rivera, C. E., & Bapna, A. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10023141 · Cited by 430
Self-citationPushing the Limits of Semi-Supervised Learning for Automatic Speech RecognitionPreprint
Zhang, Y., Qin, J., Park, D., Han, W., Chiu, C., Pang, R., Le, Q. V., & Wu, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10504 · Cited by 323
Self-citationRecent Developments on Espnet Toolkit Boosted By Conformer
Guo, P., Boyer, F., Chang, X., Hayashi, T., Higuchi, Y., Inaguma, H., Kamo, N., Li, C., Garcia-Romero, D., Shi, J., et al. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414858 · Cited by 229
Self-citationImages Speak in Images: A Generalist Painter for In-Context Visual Learning
Wang, X., Wang, W., Cao, Y., Shen, C., & Huang, T. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00660 · Cited by 243
Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models
Alexanderson, S., Nagy, R., Beskow, J., & Henter, G. E. (2023) · ACM Transactions on Graphics · doi:10.1145/3592458 · Cited by 206
Time-series anomaly detection with stacked Transformer representations and 1D convolutional network
Kim, J., Kang, H., & Kang, P. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105964 · Cited by 202
Unsupervised Speech RecognitionPreprint
Baevski, A., Hsu, W.-N., Conneau, A., & Auli, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.11084 · Cited by 207
Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training
Hsu, W.-N., Sriram, A., Baevski, A., Likhomanenko, T., Xu, Q., Pratap, V., Kahn, J., Lee, A., Collobert, R., Synnaeve, G., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-236 · Cited by 249
Pay attention to what you read: Non-recurrent handwritten text-Line recognition
Kang, L., Riba, P., Rusiñol, M., Fornés, A., & Villegas, M. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108766 · Cited by 196
Developing Real-Time Streaming Transformer Transducer for Speech Recognition on Large-Scale Dataset
Chen, X., Wu, Y., Wang, Z., Liu, S., & Li, J. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413535 · Cited by 189
Self-citationVisual speech recognition for multiple languages in the wild
Ma, P., Petridis, S., & Pantić, M. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00550-z · Cited by 190
CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement
Abdulatif, S., Cao, R., & Yang, B. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3393718 · Cited by 182
Kang, H., & Kang, P. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111507 · Cited by 188
Zhang, Y., Park, D. S., Han, W., Qin, J., Gulati, A., Shor, J., Jansen, A., Xu, Y., Huang, Y., Wang, S., et al. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3182537 · Cited by 161
Self-citationWENETSPEECH: A 10000+ Hours Multi-Domain Mandarin Corpus for Speech Recognition
Zhang, B., Lv, H., Guo, P., Shao, Q., Yang, C., Xie, L., Xu, X., Bu, H., Chen, X., Zeng, C., et al. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746682 · Cited by 156
Keyword Transformer: A Self-Attention Model for Keyword Spotting
Berg, A., O’Connor, M., & Cruz, M. T. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1286 · Cited by 179
Do Transformers Really Perform Bad for Graph Representation?Preprint
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., & Liu, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05234 · Cited by 300
Wu, H., Triebe, M. J., & Sutherland, J. W. (2023) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2023.02.018 · Cited by 147
Transformer-based Image Compression
Lu, M., Guo, P., Shi, H., Cao, C., & Ma, Z. (2022) · DCC · doi:10.1109/dcc52660.2022.00080 · Cited by 142
Auto-AVSR: Audio-Visual Speech Recognition with Automatic Labels
Ma, P., Haliassos, A., Fernandez-Lopez, A., Chen, H., Petridis, S., & Pantić, M. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096889 · Cited by 148
Wang, Y., Wang, L., & Siau, K. L. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2316376 · Cited by 163
MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification
Zhang, Y., Lv, Z., Wu, H., Zhang, S., Hu, P., Wu, Z., Lee, H.-Y., & Meng, H. M. L. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-563 · Cited by 159
Self-citationOn the Comparison of Popular End-to-End Models for Large Scale Speech Recognition
Li, J., Wu, Y., Gaur, Y., Wang, C., Zhao, R., & Liu, S. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2846 · Cited by 137
Self-citationShi, Y., Wang, Y., Wu, C., Yeh, C.-F., Chan, J., Zhang, F., Le, D., & Seltzer, M. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414560 · Cited by 150
Self-Training and Pre-Training are Complementary for Speech Recognition
Xu, Q., Baevski, A., Likhomanenko, T., Tomasello, P., Conneau, A., Collobert, R., Synnaeve, G., & Auli, M. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414641 · Cited by 152
SE-Conformer: Time-Domain Speech Enhancement Using Conformer
Kim, E., & Seo, H. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-2207 · Cited by 128
Intermediate Loss Regularization for CTC-Based Speech Recognition
Lee, J., & Watanabe, S. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414594 · Cited by 124
Using transformers for multimodal emotion recognition: Taxonomies and state of the art review
Hazmoune, S., & Bougamouza, F. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108339 · Cited by 136
Gao, Z., Zhang, S., McLoughlin, I. V., & Yan, Z. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-9996 · Cited by 242
CMGAN: Conformer-based Metric GAN for Speech Enhancement
Cao, R., Abdulatif, S., & Yang, B. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-517 · Cited by 120
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
Zvyagin, M., Brace, A., Hippe, K., Deng, Y., Zhang, B., Bohorquez, C. O., Clyde, A., Kale, B., Perez-Rivera, D., Ma, H., et al. (2023) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420231201154 · Cited by 120
Genomic language models: opportunities and challenges
Benegas, G., Ye, C., Albors, C., Li, J. C., & Song, Y. S. (2025) · Trends in Genetics · doi:10.1016/j.tig.2024.11.013 · Cited by 157
GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics
Zvyagin, M., Brace, A., Hippe, K., Deng, Y., Zhang, B., Bohorquez, C. O., Clyde, A., Kale, B., Perez-Rivera, D., Ma, H., et al. (2022) · doi:10.1101/2022.10.10.511571 · Cited by 120
Koluguri, N. R., Park, T., & Ginsburg, B. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746806 · Cited by 121
Wang, Q., Du, J., Wu, H., Pan, J., Ma, F., & Lee, C. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3256088 · Cited by 121
Ding, Y., & Jia, M. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3181933 · Cited by 120
Continuous Speech Separation with Conformer
Chen, S., Wu, Y., Chen, Z., Wu, J., Li, J., Yoshioka, T., Wang, C., Liu, S., & Zhou, M. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413423 · Cited by 118
Self-citationSub-word Level Lip Reading With Visual Attention
R, P. K., Afouras, T., & Zisserman, A. (2021) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00510 · Cited by 137
Chen, Z., Chen, J., Liu, S., Feng, Y., He, S., & Xu, E. (2022) · ISA Transactions · doi:10.1016/j.isatra.2022.04.043 · Cited by 117
Neural Architecture Search for Transformers: A Survey
Chitty-Venkata, K. T., Emani, M., Vishwanath, V., & Somani, A. K. (2022) · IEEE Access · doi:10.1109/access.2022.3212767 · Cited by 118
Audio self-supervised learning: A survey
Liu, S., Mallol-Ragolta, A., Parada‐Cabaleiro, E., Qian, K., Jing, X., Kathan, A., Hu, B., & Schuller, B. W. (2022) · Patterns · doi:10.1016/j.patter.2022.100616 · Cited by 134
FluentNet: End-to-End Detection of Stuttered Speech Disfluencies With Deep Learning
Kourkounakis, T., Hajavi, A., & Etemad, A. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3110146 · Cited by 109
Prompting Large Language Models with Speech Recognition Abilities
Fathullah, Y., Wu, C., Lakomkin, E., Jia, J., Shangguan, Y., Li, K., Guo, J., Xiong, W., Mahadeokar, J., Kalinli, O., et al. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447605 · Cited by 162
E-Branchformer: Branchformer with Enhanced Merging for Speech Recognition
Kim, K., Wu, F., Peng, Y., Pan, J. S., Sridhar, P., Han, K. J., & Watanabe, S. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10022656 · Cited by 121
WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
Zhang, B., Wu, D., Peng, Z., Song, X., Yao, Z., Lv, H., Xie, L., Yang, C., Pan, F., & Niu, J. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-483 · Cited by 101
Multi-Modal Adaptive Fusion Transformer Network for the Estimation of Depression Level
Sun, H., Liu, J., Chai, S., Qiu, Z., Lin, L., Huang, X., & Chen, Y. (2021) · Sensors · doi:10.3390/s21144764 · Cited by 104
GLiT: Neural Architecture Search for Global and Local Image Transformer
Chen, B., Li, P., Li, C., Li, B., Bai, L., Chen, L., Sun, M., Yan, J., & Ouyang, W. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00008 · Cited by 100
M2Met: The Icassp 2022 Multi-Channel Multi-Party Meeting Transcription Challenge
Yu, F., Zhang, S., Fu, Y., Xie, L., Zheng, S., Du, Z., Huang, W., Guo, P., Yan, Z., Ma, B., et al. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746465 · Cited by 96
Fast Conformer With Linearly Scalable Attention For Efficient Speech Recognition
Rekesh, D., Koluguri, N. R., Kriman, S., Majumdar, S., Noroozi, V., Huang, H., Hrinchuk, O., Puvvada, K. C., Kumar, A., Balam, J., et al. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389701 · Cited by 96
A Better and Faster end-to-end Model for Streaming ASR
Li, B., Gulati, A., Yu, J., Sainath, T. N., Chiu, C., Narayanan, A., Chang, S.-Y., Pang, R., He, Y., Qin, J., et al. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413899 · Cited by 94
Self-citationRouet‐Leduc, B., & Hulbert, C. (2024) · Nature Communications · doi:10.1038/s41467-024-47754-y · Cited by 94
SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing
Chen, W., Xing, X., Xu, X., Pang, J., & Du, L. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3235194 · Cited by 101
Progressive graph convolution network for EEG emotion recognition
Zhou, Y., Li, F., Li, Y., Li, Y., Shi, G., Zheng, W., Zhang, L., Chen, Y., & Chen, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126262 · Cited by 92
Mind the Gap: Assessing Temporal Generalization in Neural Language Models
Lazaridou, A., Kuncoro, A., Gribovskaya, E., Agrawal, D., Liska, A., Terzi, T., Gimenez, M., d'Autume, C. D. M., Kocisky, T., Ruder, S., et al. (2021) · arXiv · doi:10.48550/arxiv.2102.01951 · Cited by 90
Ensemble Multifeatured Deep Learning Models and Applications: A Survey
Abimannan, S., El-Alfy, E.-S. M., Chang, Y., Hussain, S., Shukla, S., & Satheesh, D. (2023) · IEEE Access · doi:10.1109/access.2023.3320042 · Cited by 107
Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition for Sound Event Detection
Nam, H., Kim, S.-H., Ko, B.-Y., & Park, Y. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10127 · Cited by 101
Wang, L., Zhang, M., Gao, X., & Shi, W. (2024) · Remote Sensing · doi:10.3390/rs16050804 · Cited by 100
MuIT: An End-to-End Multitask Learning Transformer
Bhattacharjee, D., Zhang, T., Susstrunk, S. E., & Salzmann, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01172 · Cited by 101
Efficient Conformer: Progressive Downsampling and Grouped Attention for Automatic Speech Recognition
Burchi, M., & Vielzeuf, V. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9687874 · Cited by 83
Detecting Deepfake Voice Using Explainable Deep Learning Techniques
Lim, S.-Y., Chae, D.-K., & Lee, S.-C. (2022) · Applied Sciences · doi:10.3390/app12083926 · Cited by 82
Tang, J., Ma, Z., Gan, K., Zhang, J., & Yin, Z. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102129 · Cited by 87
HINT: High-Quality INpainting Transformer With Mask-Aware Encoding and Enhanced Attention
Chen, S., Atapour–Abarghouei, A., & Shum, H. P. H. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3369897 · Cited by 86
A generic non-invasive neuromotor interface for human-computer interaction
Kaifosh, P., Reardon, T. R., Labs, C.-L. A. R., Allen, B. D., Anderson, C., Arnoud, S., Arora, R., Atray, M., Awad, L., Ayerbe, F., et al. (2025) · Nature · doi:10.1038/s41586-025-09255-w · Cited by 158
Yang, Z., Zhou, D., Yang, Y., Zhang, J., & Chen, Z. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3171973 · Cited by 85
Findings of the IWSLT 2022 Evaluation Campaign
Anastasopoulos, A., Barrault, L., Bentivogli, L., Boito, M. Z., Bojar, O., Cattoni, R., Currey, A., Dinu, G., Duh, K., Elbayad, M., et al. (2022) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2022.iwslt-1.10 · Cited by 82
Mamba in Speech: Towards an Alternative to Self-Attention
Zhang, X., Zhang, Q., Liu, H., Xiao, T., Qian, X., Ahmed, B., Eliathamby Ambikairajah, A., Li, H., & Epps, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3566210 · Cited by 117
SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network
Chan, W., Park, D., Lee, C., Zhang, Y., Le, Q., & Norouzi, M. (2021) · arXiv · doi:10.48550/arxiv.2104.02133 · Cited by 79
Self-citationQuan, C., & Li, X. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3357036 · Cited by 78
Attention mechanisms and deep learning for machine vision: A survey of the state of the artPreprint
Hafiz, A. M., Parah, S. A., & Bhat, R. U. A. (2021) · Research Square · doi:10.21203/rs.3.rs-510910/v1 · Cited by 92
Zhao, S., & Ma, B. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096646 · Cited by 87
Unsupervised Automatic Speech Recognition: A review
Aldarmaki, H., Ullah, A., Ram, S., & Zaki, N. (2022) · Speech Communication · doi:10.1016/j.specom.2022.02.005 · Cited by 92
Zhao, J. S., & Zhang, W.-Q. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3184480 · Cited by 84
Metadata
- Authors
- Gulati, A., Qin, J., Chiu, C.-C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y., & Pang, R.
- Affiliations
- Google (United States) (US)
- ORCID
- Venue
- Interspeech 2020
- Publisher
- ISCA
- Published
- 2020-10-25
- Pages
- 5036-5040
- ISSN
- 2308-457X (ISSN-L)
- OpenAlex
- W3097777922
- Cited by
- 3,150
- References
- 33
- Citations by year
- 2026: 373 · 2025: 666 · 2024: 644 · 2023: 708 · 2022: 424 · 2021: 300 · 2020: 35
- FWCI
- 140.10
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- public-domain
- Locations
- Interspeech (USB) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- conformational isomers, speech recognition, CNN-Transformer hybrid, parameter-efficient models, LibriSpeech, word error rate
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
