Conformer: Convolution-augmented Transformer for Speech Recognition

Attention Is All You Need

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

Adam: 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

Untitled work

(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

Transformer-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

Context-aware data augmentation for enhanced speech command recognition in industrial environments

Simone, G. D., Greco, A., Rosa, F., Saggese, A., & Vento, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-01886-3 · Cited by 13

Performance Evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward

Kumar, S., Thorbecke, I., Burdisso, S., Villatoro-Tello, E., Manjunath, K. E., Hacıoğlu, K., Rangappa, P., Motlíček, P., Ganapathiraju, A., & Stolcke, A. (2025) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw65056.2025.11010998 · Cited by 14

Machine Learning Transition State Geometries and Applications in Reaction Property Prediction

Beaglehole, I. W., Pemberton, M. J., Farrar, E. H. E., & Grayson, M. N. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70025 · Cited by 13

ELAFormer: Early Local Attention in multi-scale vision transFormers

Zhou, X., Ren, Z., Zhang, Y., Jiang, Z., Yu, T., Luo, H., & Zhou, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113851 · Cited by 14

Deep learning-based in-ambulance speech recognition and generation of prehospital emergency diagnostic summaries using LLMs

Chen, C., Hu, Y., Cai, W., Pan, H., Shen, M., Zhai, Y., Wu, S. X., Zhou, Q., & Guo, Y. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106029 · Cited by 10

A first-order meta learning method for remaining useful life prediction of rotating machinery under limited samples

Wang, Y., Liu, S., Lv, S., & Liu, G. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113616 · Cited by 11

Multi-Input Multi-Output Target-Speaker Voice Activity Detection for Unified, Flexible, and Robust Audio-Visual Speaker Diarization

Cheng, M., & Li, M. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3597450 · Cited by 12

Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition

Zhou, W. G., Zeineldeen, M., Zheng, Z., Schlüter, R., & Ney, H. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1623 · Cited by 12

Predictive Maintenance for General Aviation Using Convolutional Transformers

Yang, H. Y., LaBella, A., & Desell, T. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21538 · Cited by 16

Lightweight Convolutional-iConformer for Sound Event Detection

Chan, T. K., & Chin, C. S. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3173582 · Cited by 10

ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks

Boito, M. Z., Ortega, J., Riguidel, H., Laurent, A., Barrault, L., Bougares, F., Chaabani, F., Nguyen, H., Barbier, F., Gahbiche, S., et al. (2022) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2022.iwslt-1.28 · Cited by 15

Learning Speaker-specific Lip-to-Speech Generation

Varshney, M., Yadav, R., Namboodiri, V. P., & Hegde, R. M. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956600 · Cited by 10

Synthetic Voice Detection and Audio Splicing Detection using SE-Res2Net-Conformer Architecture

Wang, L., Yeoh, B., & Ng, J. W. (2022) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp57327.2022.10037999 · Cited by 15

Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-to-End Automatic Speech Recognition

Laptev, A., & Ginsburg, B. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10022960 · Cited by 10

Compressed Collective Sparse-Sketch for Distributed Data-Parallel Training of Deep Learning Models

Ge, K., Lü, K., Fu, Y., Deng, X., Lai, Z., & Li, D. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2023.3242733 · Cited by 10

Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models

Hernandez, S. M., Zhao, D. X., Ding, S., Bruguier, A., Prabhavalkar, R., Sainath, T. N., He, Y., & Mcgraw, I. C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095006 · Cited by 10

Adaptable End-to-End ASR Models Using Replaceable Internal LMs and Residual Softmax

Deng, K., & Woodland, P. C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095860 · Cited by 10

Speech Intelligibility Classifiers from 550k Disordered Speech Samples

Venugopalan, S., Tobin, J., Yang, S., Seaver, K., Cave, R., Jiang, P.-P., Zeghidour, N., Heywood, R., Green, J. R., & Brenner, M. P. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095933 · Cited by 11

Any-to-Any Voice Conversion with F0 and Timbre Disentanglement and Novel Timbre Conditioning

Kovela, S., Valle, R., Dantrey, A., & Catanzaro, B. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096220 · Cited by 10

Diagonal State Space Augmented Transformers for Speech Recognition

Saon, G., Gupta, A., & Cui, X. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096271 · Cited by 10

Silent Speech Interface With Vocal Speaker Assistance Based on Convolution-Augmented Transformer

Li, H., Liang, Y., Gao, H., Liu, L., Wang, Y., Chen, D., Luo, Z., & Li, G. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3273660 · Cited by 11

SC-CNN: Effective Speaker Conditioning Method for Zero-Shot Multi-Speaker Text-to-Speech Systems

Yoon, H., Kim, C., Um, S., Yoon, H.-W., & Kang, H.-G. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3277786 · Cited by 11

Combining AI and AM – Improving approximate matching through transformer networks

Uhlig, F., Struppek, L., Hintersdorf, D., Göbel, T., Baier, H., & Kersting, K. (2023) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2023.301570 · Cited by 12

Show Me a Video: A Large-Scale Narrated Video Dataset for Coherent Story Illustration

Lu, Y., Ni, F., Wang, H., Guo, X., Zhu, L., Yang, Z., Song, R., Cheng, L., & Yang, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3296944 · Cited by 10

Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks

Tang, Y., Sun, A., Inaguma, H., Chen, X., Dong, N., Ma, X., Tomasello, P., & Pino, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.695 · Cited by 16

Does Simultaneous Speech Translation need Simultaneous Models?

Papi, S., Gaido, M., Negri, M., & Turchi, M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.11 · Cited by 10

Outlier-aware Inlier Modeling and Multi-scale Scoring for Anomalous Sound Detection via Multitask Learning

Zhang, Y., Hongbin, S., Wan, Y., & Li, M. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-572 · Cited by 10

Methods for processing and analyzing passive acoustic monitoring data: An example of song recognition in western black-crested gibbons

Zhou, X., Hu, K., Guan, Z., Yu, C., Wang, S., Fan, M., Sun, Y., Cao, Y., Wang, Y., & Miao, G. (2023) · Ecological Indicators · doi:10.1016/j.ecolind.2023.110908 · Cited by 10

A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognition

Barcovschi, A., Jain, R., & Corcoran, P. (2023) · International Conference on Speech Technology and Human-Computer Dialogue (SpeD) · doi:10.1109/sped59241.2023.10314867 · Cited by 13

A Lightweight CNN-Conformer Model for Automatic Speaker Verification

Wang, H., Lin, X., & Zhang, J. (2023) · IEEE Signal Processing Letters · doi:10.1109/lsp.2023.3342714 · Cited by 10

LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of End-to-End ASR Models

Meister, A., Novikov, M., Karpov, N., Bakhturina, E., Lavrukhin, V., & Ginsburg, B. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389666 · Cited by 27

Enhancing Speaker Diarization with Large Language Models: A Contextual Beam Search Approach

Park, T., Dhawan, K., Koluguri, N. R., & Balam, J. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446204 · Cited by 17

Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks

Du, Y., Zhang, Z., Yue, L., Huang, X., Zhang, Y., Xu, T., Xu, L., & Chen, E. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447662 · Cited by 10

FT-GAN: Fine-Grained Tune Modeling for Chinese Opera Synthesis

Zheng, M., Bai, P., Shi, X., Zhou, X. S., & Yan, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29943 · Cited by 10

A Comparative Study on Neural Architectures and Training Methods for Japanese Speech Recognition

Karita, S., Kubo, Y., Bacchiani, M., & Jones, L. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-775 · Cited by 11

Study of Positional Encoding Approaches for Audio Spectrogram Transformers

Pepino, L., Riera, P., & Ferrer, L. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747742 · Cited by 11

Multi-Scale Features for Transformer Model to Improve the Performance of Sound Event Detection

Kim, S.-J., & Chung, Y.-J. (2022) · Applied Sciences · doi:10.3390/app12052626 · Cited by 10

Conversational Speech Recognition by Learning Conversation-Level Characteristics

Wei, K., Zhang, Y., Sun, S., Xie, L., & Ma, L. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746884 · Cited by 10

Improving the Fusion of Acoustic and Text Representations in RNN-T

Zhang, C., Li, B., Lu, Z., Sainath, T. N., & Chang, S.-Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747418 · Cited by 11

Phone-Informed Refinement of Synthesized Mel Spectrogram for Data Augmentation in Speech Recognition

Ueno, S., & Kawahara, T. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746582 · Cited by 10

Boundary and Context Aware Training for CIF-Based Non-Autoregressive End-to-End ASR

Yu, F., Luo, H., Guo, P., Liang, Y., Yao, Z., Xie, L., Gao, Y., Hou, L., & Zhang, S. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688238 · Cited by 10

Non-Autoregressive Mandarin-English Code-Switching Speech Recognition

Chuang, S.-P., Chang, H.-J., Huang, S.-F., & Lee, H.-Y. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688174 · Cited by 10

Self-Supervised Pre-Trained Speech Representation Based End-to-End Mispronunciation Detection and Diagnosis of Mandarin

Shen, Y., Liu, Q., Fan, Z., Liu, J., & Wumaier, A. (2022) · IEEE Access · doi:10.1109/access.2022.3212417 · Cited by 11

Exploring the Role of Language Families for Building Indic Speech Synthesisers

Prakash, A., & Murthy, H. A. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3230453 · Cited by 13

SASE: Self-Adaptive noise distribution network for Speech Enhancement with Federated Learning using heterogeneous data

Lin, Z., Zeng, B., Hu, H., Huang, Y., Xu, L., & Yao, Z. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110396 · Cited by 13

Real-time Context-Aware Multimodal Network for Activity and Activity-Stage Recognition from Team Communication in Dynamic Clinical Settings

Gao, C., Marsic, I., Sarcevic, A., Gestrich-Thompson, W. V., & Burd, R. S. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580798 · Cited by 10

Leveraging Large Text Corpora For End-To-End Speech Summarization

Matsuura, K., Ashihara, T., Moriya, T., Tanaka, T., Ogawa, A., Delcroix, M., & Masumura, R. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094993 · Cited by 11

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation

Huang, R., Liu, H., Cheng, X., Ren, Y., Li, L., Ye, Z., He, J., Zhang, L., Liu, J., Yin, X., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.479 · Cited by 16

The IMS Toucan System for the Blizzard Challenge 2023

Lux, F., Koch, J., Meyer, S., Bott, T., Schauffler, N., Denisov, P., Schweitzer, A., & Vu, N. T. (2023) · doi:10.21437/blizzard.2023-4 · Cited by 10

Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberationPreprint

Fu, Y., Liu, Y., Li, J., Luo, D., Lv, S., Jv, Y., & Xie, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.06015 · Cited by 10

The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation

Li, Y., Wang, M., Guo, J., Qiao, X., Wang, Y., Wei, D., Su, C., Chen, Y., Zhang, M., Tao, S., et al. (2022) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2022.iwslt-1.20 · Cited by 9

A Language Agnostic Multilingual Streaming On-Device ASR System

Li, B., Sainath, T. N., Pang, R., Chang, S.-Y., Xu, Q., Strohman, T., Chen, V., Liang, Q., Liu, H., He, Y., et al. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10006 · Cited by 9

Transformer-Based Lip-Reading with Regularized Dropout and Relaxed Attention

Li, Z., Lohrenz, T., Dunkelberg, M., & Fingscheidt, T. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10023442 · Cited by 10

Explainability of Speech Recognition Transformers via Gradient-Based Attention Visualization

Sun, T., Chen, H., Hu, G., He, L., & Zhao, C. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3282488 · Cited by 9

LTAnomaly: A Transformer Variant for Syslog Anomaly Detection Based on Multi-Scale Representation and Long Sequence Capture

Han, D., Sun, M., Li, M., & Chen, Q. (2023) · Applied Sciences · doi:10.3390/app13137668 · Cited by 9

Adapting an ASR Foundation Model for Spoken Language Assessment

Ma, R., Qian, M., Gales, M., & Knill, K. M. (2023) · ISCA Workshop on Speech and Language Technology in Education (SLaTE) · doi:10.21437/slate.2023-20 · Cited by 17

Lip2Vec: Efficient and Robust Visual Speech Recognition via Latent-to-Latent Visual to Audio Representation Mapping

Djilali, Y. A. D., Narayan, S., Boussaid, H., Almazrouei, E., & Debbah, M. A. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01268 · Cited by 10

Transformer Attractors for Robust and Efficient End-To-End Neural Diarization

Samarakoon, L., Broughton, S. J., Härkönen, M., & Fung, I. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389679 · Cited by 10

Learning From Flawed Data: Weakly Supervised Automatic Speech Recognition

Gao, D., Xu, H., Raj, D., Garcia, L. P., Povey, D., & Khudanpur, S. P. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389684 · Cited by 10

CGMMA: CNN-GNN Multiscale Mixed Attention Network for Remote Sensing Image Change Detection

Zhang, Y., Song, X., Hua, Z., & Li, J. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3358298 · Cited by 9

Fault Detection of In‐Service Bridge Expansion Joint Based on Voiceprint Recognition

Dong, Y., Wang, D., Pan, Y., Di, J., & Chen, A. (2024) · Structural Control and Health Monitoring · doi:10.1155/2024/1270912 · Cited by 9

TDT-KWS: Fast and Accurate Keyword Spotting Using Token-and-Duration Transducer

Xi, Y., Li, H., Yang, B., Li, H., Xu, H., & Yu, K. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446909 · Cited by 9

Real-Time Sound Recognition System for Human Care Robot Considering Custom Sound Events

Kim, S.-H., Nam, H., Choi, S., & Park, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3378096 · Cited by 10

Low-Contrast Medical Image Segmentation via Transformer and Boundary Perception

Zhang, Y., Xi, R., Wang, W., Li, H., Hu, L., Lin, H., Towey, D., Bai, R., Fu, H., Higashita, R., et al. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3353624 · Cited by 10

Advanced accent/dialect identification and accentedness assessment with multi-embedding models and automatic speech recognition

Ghorbani, S., & Hansen, J. H. L. (2024) · The Journal of the Acoustical Society of America · doi:10.1121/10.0026235 · Cited by 9

A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction

Xiao, J., & Lu, P. (2024) · Applied Sciences · doi:10.3390/app14146139 · Cited by 9

Robust speech command recognition in challenging industrial environments

Bini, S., Carletti, V., Saggese, A., & Vento, M. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.107938 · Cited by 9

Reparameterization of Lightweight Transformer for On-Device Speech Emotion Recognition

Zhang, Z., Dong, Z., Xu, W., & Han, J. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3483232 · Cited by 10

Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers

Cappellazzo, U., Falavigna, D., Brutti, A., & Ravanelli, M. (2024) · Machine learning for signal processing ... · doi:10.1109/mlsp58920.2024.10734776 · Cited by 9

GCRTcall: a transformer based basecaller for nanopore RNA sequencing enhanced by gated convolution and relative position embedding via joint loss training

Li, Q., Sun, C., Wang, D., & Lou, J. (2024) · Frontiers in Genetics · doi:10.3389/fgene.2024.1443532 · Cited by 9

An overview of high-resource automatic speech recognition methods and their empirical evaluation in low-resource environments

Fatehi, K., Torres, M. T., & Küçükyılmaz, A. (2024) · Speech Communication · doi:10.1016/j.specom.2024.103151 · Cited by 12

Enhancing bowel sound recognition with self-attention and self-supervised pre-training

Yu, Y., Zhang, M., Xie, Z., & Liu, Q. (2024) · PLoS ONE · doi:10.1371/journal.pone.0311503 · Cited by 12

Audio-Visual Learning for Multimodal Emotion Recognition

Fan, S., Jing, J., & Wang, C. (2025) · Symmetry · doi:10.3390/sym17030418 · Cited by 12

Speech translation for multilingual medical education leveraged by large language models

Iranzo-Sánchez, J., Santamaría-Jordà, J., Mas-Mollà, G., Díaz-Munío, G. V. G., Iranzo-Sánchez, J., Jorge, J., Silvestre-Cerdà, J. A., Giménez, A., Civera, J., Sanchís, A., et al. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103147 · Cited by 12

PSELDNets: Pre-Trained Neural Networks on a Large-Scale Synthetic Dataset for Sound Event Localization and Detection

Hu, J., Cao, Y. P., Wu, M., Kang, F., Yang, F., Wang, W., Plumbley, M. D., & Yang, J. J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3587446 · Cited by 12

Optimizing chlorophyll content prediction in tea leaves via spectral transformations and deep learning

Tsuchiya, Y., Yoshida, K., Ishiguro, Y., Kawaki, J., Yamashita, H., Ikka, T., & Sonobe, R. (2025) · BMC Plant Biology · doi:10.1186/s12870-025-07863-2 · Cited by 9

Harmonising convolutional networks and Transformer for transfer learning in bearing fault diagnosis

Su, K., Ran, G., Li, X., Wang, T., Yang, Y., CHU, F., & Kong, X. (2026) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2606949 · Cited by 9

Analysis of Automatic Speech Recognition for Domain-Specific Contents

Niyogi, A., Sandeep, J., & Kumar, B. (2026) · Springer eBooks · doi:10.1007/978-981-96-9650-5_6 · Cited by 9

Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolóxochitl Mixtec

Shi, J., Amith, J. D., García, R. C., Sierra, E. G., Duh, K., & Watanabe, S. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.96 · Cited by 9

Exploring Non-Autoregressive End-to-End Neural Modeling for English Mispronunciation Detection and Diagnosis

Wang, H.-W., Yan, B.-C., Chiu, H.-S., Hsu, Y.-C., & Chen, B. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747569 · Cited by 9

Multi-Channel Multi-Speaker ASR Using 3D Spatial Feature

Shao, Y., Zhang, S., & Yu, D. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746678 · Cited by 9

ICASSP 2022 L3DAS22 Challenge: Ensemble of Resnet-Conformers with Ambisonics Data Augmentation for Sound Event Localization and Detection

Mao, Y., Zeng, Y., Liu, H., Zhu, W., & Zhou, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746673 · Cited by 9

Conformer-Based Speech Recognition with Linear Nyström Attention and Rotary Position Embedding

Samarakoon, L., & Leung, T.-Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747691 · Cited by 9

Residual Language Model for End-to-end Speech Recognition

Tsunoo, E., Kashiwagi, Y., Narisetty, C. P., & Watanabe, S. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10557 · Cited by 9

FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition

Chen, S.-J., Xie, J., & Hansen, J. H. L. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10917 · Cited by 9

CGA-MGAN: Metric GAN Based on Convolution-Augmented Gated Attention for Speech Enhancement

Chen, H., & Zhang, X. (2023) · Entropy · doi:10.3390/e25040628 · Cited by 8

Structured State Space Decoder for Speech Recognition and Synthesis

Miyazaki, K., Murata, M., & Koriyama, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096135 · Cited by 9

TrimTail: Low-Latency Streaming ASR with Simple But Effective Spectrogram-Level Length Penalty

Song, X., Wu, D., Wu, Z., Zhang, B., Zhang, Y., Peng, Z., Li, W., Pan, F., & Zhu, C. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097012 · Cited by 9

Fast and Accurate Factorized Neural Transducer for Text Adaption of End-to-End Speech Recognition Models

Zhao, R., Xue, J., Parthasarathy, P., Miljanic, V., & Li, J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096895 · Cited by 9

Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems: A Case Study for Modern Greek

Paraskevopoulos, G., Kouzelis, T., Rouvalis, G., Katsamanis, A., Katsouros, V., & Potamianos, A. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3328280 · Cited by 9

Employing convolution-enhanced attention mechanisms for earthquake detection and phase picking models

Wang, S., Liu, F., Yin, X.-X., Chen, K., & Cai, R. (2023) · Frontiers in Earth Science · doi:10.3389/feart.2023.1283857 · Cited by 10

AdVerb: Visually Guided Audio Dereverberation

Chowdhury, S., Ghosh, S., Dasgupta, S., Ratnarajah, A. J., Tyagi, U., & Manocha, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00725 · Cited by 9

Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detection

Lian, J., Feng, C., Farooqi, N., Li, S., Kashyap, A., Cho, C. J., Wu, P., Netzorg, R., Li, T., & Anumanchipalli, G. K. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389771 · Cited by 9

Temporal Shift Module with Pretrained Representations for Speech Emotion Recognition

Shen, S., Liu, F., Wang, H., Wang, Y., & Zhou, A. (2024) · Intelligent Computing · doi:10.34133/icomputing.0073 · Cited by 10

Exploring Phonetic Context-Aware Lip-Sync for Talking Face Generation

Park, S. J., Kim, M., Choi, J., & Ro, Y. M. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447284 · Cited by 9

An MVDR-Embedded U-Net Beamformer for Effective and Robust Multichannel Speech Enhancement

Lee, C.-H., Patel, K., Yang, C. J., Shen, Y., & Jin, H. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448366 · Cited by 9

Noise-Robust Multilingual Speech Recognition and the Tatar Speech Corpus

Mussakhojayeva, S., Абрекович, Г. Р., Эрнстович, Х. Б., Galimov, M., Orel, D., Abilbekov, A., & Varol, H. A. (2024) · International Conference on Artificial Intelligence in Information and Communication (ICAIIC) · doi:10.1109/icaiic60209.2024.10463419 · Cited by 9

Fast offline transformer‐based end‐to‐end automatic speech recognition for real‐world applications

Oh, Y. R., Park, K., & Park, J. G. (2021) · ETRI Journal · doi:10.4218/etrij.2021-0106 · Cited by 9

Multitask Training with Text Data for End-to-End Speech Recognition

Wang, P., Sainath, T. N., & Weiss, R. J. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-683 · Cited by 9

SPLICEOUT: A Simple and Efficient Audio Augmentation Method

Jain, A., Samala, P. R., Mittal, D., Jyothi, P., & Singh, M. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-572 · Cited by 10

Enhancing Speech Recognition: A Fusion of Transformers with Convolutional Context

Jadhav, A., Patil, S. K., Ekbote, J., Chikkamath, S., & Nirmala, S. R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31526-7_13

MULTI-CONVFORMER: Extending Conformer with Multiple Convolution Kernels

Prabhu, D., Peng, Y., Jyothi, P., & Watanabe, S. (2024) · Interspeech (USB) · doi:10.21437/interspeech.2024-2384 · Cited by 8

Application of the conformer model for kazakh speech recognition

Mamyrbayev, O. J., Kurmetkan, T., & Arslan, R. S. (2024) · Bulletin of the National Engineering Academy of the Republic of Kazakhstan · doi:10.47533/2024.1606-146x.57 · Cited by 1

ConvRNN-T: Convolutional Augmented Recurrent Neural Network Transducers for Streaming Speech Recognition

Radfar, M. H., Barnwal, R., Swaminathan, R. V., Chang, F.-J., Strimel, G. P., Susanj, N., & Mouchtaris, A. (2022) · Interspeech · doi:10.21437/interspeech.2022-10844 · Cited by 16

Speech recognition system of transformer improved by pre-parallel convolution Neural Network

Qi, Y., Zhan, H., Chu, J., Han, X., Li, P., & Deng, X. (2022) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma54519.2022.9855999