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

Watch or Listen: Robust Audio-Visual Speech Recognition with Visual Corruption Modeling and Reliability Scoring

Hong, J. H., Kim, M., Choi, J., & Ro, Y. M. (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/cvpr52729.2023.01801 · Cited by 43

AI‐based language tutoring systems with end‐to‐end automatic speech recognition and proficiency evaluation

Kang, B., Jeon, H., & Lee, Y. K. (2024) · ETRI Journal · doi:10.4218/etrij.2023-0322 · Cited by 42

CVTNet: A Fusion of Convolutional Neural Networks and Vision Transformer for Wetland Mapping Using Sentinel-1 and Sentinel-2 Satellite Data

Marjani, M., Mahdianpari, M., Mohammadimanesh, F., & Gill, E. W. (2024) · Remote Sensing · doi:10.3390/rs16132427 · Cited by 42

Empirical Analysis of Honeybees Acoustics as Biosensors Signals for Swarm Prediction in Beehives

Iqbal, K., Alabdullah, B. I., Mudawi, N. A., Algarni, A. M., Jalal, A., & Park, J. (2024) · IEEE Access · doi:10.1109/access.2024.3471895 · Cited by 42

DF-Conformer: Integrated Architecture of Conv-Tasnet and Conformer Using Linear Complexity Self-Attention for Speech Enhancement

Koizumi, Y., Karita, S., Wisdom, S., Erdogan, H., Hershey, J., Jones, L., & Bacchiani, M. (2021) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics · doi:10.1109/waspaa52581.2021.9632794 · Cited by 50

An Encoder-Decoder Approach to Handwritten Mathematical Expression Recognition with Multi-head Attention and Stacked Decoder

Ding, H., Chen, K., & Huo, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86331-9_39 · Cited by 44

TRILLsson: Distilled Universal Paralinguistic Speech Representations

Shor, J., & Venugopalan, S. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-118 · Cited by 42

Points to Patches: Enabling the Use of Self-Attention for 3D Shape Recognition

Berg, A., Oskarsson, M., & O'Connor, M. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956172 · Cited by 42

Cropformer: A new generalized deep learning classification approach for multi-scenario crop classification

Wang, H., Chang, W., Yao, Y., Yao, Z., Zhao, Y., Li, S., Liu, Z., & Zhang, X. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1130659 · Cited by 46

Real-Time Target Sound Extraction

Veluri, B., Chan, J., Itani, M., Chen, T., Yoshioka, T., & Gollakota, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094573 · Cited by 41

SpeechX: Neural Codec Language Model as a Versatile Speech Transformer

Wang, X., Thakker, M., Chen, Z., Kanda, N., Eskimez, Ş. E., Chen, S., Tang, M., Liu, S., Li, J., & Yoshioka, T. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3419418 · Cited by 83

ACT: an Attentive Convolutional Transformer for Efficient Text Classification

Li, P., Zhong, P., Mao, K., Wang, D., Yang, X., Liu, Y., Yin, J., & See, S. C. W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i15.17566 · Cited by 44

Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset

Yang, Z., Chen, Y., Luo, L., Yang, R., Ye, L., Cheng, G., Xu, J., Jin, Y., Zhang, Q., Zhang, P., et al. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-729 · Cited by 51

PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech RecognitionPreprint

Lai, C.-I., Zhang, Y., Liu, A. H., Chang, S., Liao, Y.-L., Chuang, Y.-S., Qian, K., Khurana, S., Cox, D., & Glass, J. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.05933 · Cited by 45

Transcribe-to-Diarize: Neural Speaker Diarization for Unlimited Number of Speakers Using End-to-End Speaker-Attributed ASR

Kanda, N., Xiao, X., Gaur, Y., Wang, X., Meng, Z., Chen, Z., & Yoshioka, T. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746225 · Cited by 40

Joint Unsupervised and Supervised Training for Multilingual ASR

Bai, J., Li, B., Zhang, Y., Bapna, A., Siddhartha, N., Sim, K. C., & Sainath, T. N. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746038 · Cited by 41

Towards Measuring Fairness in Speech Recognition: Casual Conversations Dataset Transcriptions

Liu, C., Picheny, M., Sarı, L., Chitkara, P., Xiao, A., Zhang, X., Chou, M., Alvarado, A., Hazırbaş, C., & Saraf, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747501 · Cited by 42

AISHELL-NER: Named Entity Recognition from Chinese Speech

Chen, B., Xu, G., Wang, X., Xie, P., Zhang, M., & Huang, F. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746955 · Cited by 41

CTGAN : Cloud Transformer Generative Adversarial Network

Huang, G.-L., & Wu, P.-Y. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897229 · Cited by 40

Reproducing Whisper-Style Training Using An Open-Source Toolkit And Publicly Available Data

Peng, Y., Tian, J., Yan, B., Berrebbi, D., Chang, X., Li, X., Shi, J., Arora, S., Chen, W., Sharma, R., et al. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389676 · Cited by 55

XLSR-Mamba: A Dual-Column Bidirectional State Space Model for Spoofing Attack Detection

Xiao, Y., & Das, R. K. (2025) · IEEE Signal Processing Letters · doi:10.1109/lsp.2025.3547861 · Cited by 54

IBGS: A Wearable Smart System to Assist Visually Challenged

Xia, K., Li, X., Liu, H., Zhou, M., & Zhu, K. (2022) · IEEE Access · doi:10.1109/access.2022.3193097 · Cited by 39

Exploring Self-Supervised Pre-Trained ASR Models for Dysarthric and Elderly Speech Recognition

Hu, S., Xie, X., Jin, Z., Geng, M., Wang, Y., Cui, M., Deng, J., Liu, X., & Meng, H. M. L. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097275 · Cited by 39

Spatio-temporal representation learning enhanced speech emotion recognition with multi-head attention mechanisms

Chen, Z., Lin, M., Wang, Z., Zheng, Q., & Liu, C. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111077 · Cited by 39

Versatile Audio-Visual Learning for Emotion Recognition

Goncalves, L., Leem, S.-G., Lin, W.-C., Şişman, B., & Busso, C. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3433386 · Cited by 51

Efficient Knowledge Distillation for RNN-Transducer Models

Panchapagesan, S., Park, D., Chiu, C., Shangguan, Y., Liang, Q., & Gruenstein, A. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413905 · Cited by 39

Improved Language Identification Through Cross-Lingual Self-Supervised Learning

Tjandra, A., Choudhury, D. G., Zhang, F., Singh, K., Conneau, A., Baevski, A., Sela, A., Saraf, Y., & Auli, M. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747667 · Cited by 40

Semi-supervised Vision Transformers

Weng, Z., Yang, X., Li, A., Wu, Z., & Jiang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20056-4_35 · Cited by 42

Primer: Searching for Efficient Transformers for Language ModelingPreprint

So, D. R., Mańke, W., Liu, H., Dai, Z., Shazeer, N., & Le, Q. V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08668 · Cited by 91

Mitigating bias against non-native accents

Zhang, Y., Zhang, Y., Halpern, B. M., Patel, T. B., & Scharenborg, O. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-836 · Cited by 38

Multilingual Speech Recognition for Turkic Languages

Mussakhojayeva, S., Dauletbek, K., Yeshpanov, R., & Varol, H. A. (2023) · Information · doi:10.3390/info14020074 · Cited by 38

Voice Interaction Recognition Design in Real-Life Scenario Mobile Robot Applications

Li, S., Liu, Y., Chen, Y.-C., Feng, H.-M., Shen, P.-K., & Wu, Y.-C. (2023) · Applied Sciences · doi:10.3390/app13053359 · Cited by 41

METTS: Multilingual Emotional Text-to-Speech by Cross-Speaker and Cross-Lingual Emotion Transfer

Zhu, X., Lei, Y., Li, T., Zhang, Y., Zhou, H., Lu, H., & Xie, L. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3363444 · Cited by 38

Self-Supervised ASR Models and Features for Dysarthric and Elderly Speech Recognition

Hu, S., Xie, X., Geng, M., Jin, Z., Deng, J., Li, G., Wang, Y., Cui, M., Wang, T., Meng, H. M. L., et al. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3422839 · Cited by 40

Optical multilayer thin film structure inverse design: From optimization to deep learning

Ma, T., Ma, M., & Guo, L. J. (2025) · iScience · doi:10.1016/j.isci.2025.112222 · Cited by 44

MuQ: Self-Supervised Music Representation Learning With Mel Residual Vector Quantization

Zhu, H., Zhou, Y., Chen, H., Yu, J., Ma, Z., Gu, R., Luo, Y., Tan, W., & Chen, X. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3602320 · Cited by 110

Deep Learning for Oil Palm Fruit Ripeness Classification with DenseNet

Herman, H., Cenggoro, T. W., Susanto, A., & Pardamean, B. (2021) · International Conference on Information Management and Technology (ICIMTech) · doi:10.1109/icimtech53080.2021.9534988 · Cited by 38

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition

Leng, Y., Tan, X., Wang, R., Zhu, L., Xu, J., Liu, W., Liu, L., Li, X., Qin, T., Lin, E. C., et al. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.367 · Cited by 38

Target-Speaker Voice Activity Detection Via Sequence-to-Sequence Prediction

Cheng, M., Wang, W., Zhang, Y., Qin, X., & Li, M. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094752 · Cited by 38

Multi‐channel response reconstruction using transformer based generative adversarial network

Zheng, W., Li, J., Li, Q., & Hao, H. (2023) · Earthquake Engineering & Structural Dynamics · doi:10.1002/eqe.3960 · Cited by 37

The 2020 ESPnet Update: New Features, Broadened Applications, Performance Improvements, and Future Plans

Watanabe, S., Boyer, F., Chang, X., Guo, P., Hayashi, T., Higuchi, Y., Hori, T., Huang, W.-C., Inaguma, H., Kamo, N., et al. (2021) · IEEE Data Science Workshop (DSW) · doi:10.1109/dslw51110.2021.9523402 · Cited by 37

AST: Audio Spectrogram TransformerPreprint

Gong, Y., Chung, Y.-A., & Glass, J. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01778 · Cited by 447

FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech RecognitionPreprint

Leng, Y., Tan, X., Zhu, L., Xu, J., Luo, R., Liu, L., Qin, T., Li, X., Lin, E., & Liu, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03842 · Cited by 37

Anonymizing Speech with Generative Adversarial Networks to Preserve Speaker Privacy

Meyer, S., Tilli, P., Denisov, P., Lux, F., Koch, J., & Vu, N. T. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10022601 · Cited by 38

Deep Demand Prediction: An Enhanced Conformer Model With Cold-Start Adaptation for Origin–Destination Ride-Hailing Demand Prediction

Lin, H., He, Y., Liu, Y., Gao, K., & Qu, X. (2023) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2023.3309653 · Cited by 36

CASS-NAT: CTC Alignment-Based Single Step Non-Autoregressive Transformer for Speech Recognition

Fan, R., Chu, W., Chang, P., & Xiao, J. Z. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413429 · Cited by 36

Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation

Inaguma, H., Kawahara, T., & Watanabe, S. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.150 · Cited by 39

End-to-End Neural Diarization: From Transformer to Conformer

Liu, Y. C., Han, E., Lee, C., & Stolcke, A. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1909 · Cited by 36

SPGISpeech: 5,000 Hours of Transcribed Financial Audio for Fully Formatted End-to-End Speech Recognition

O’Neill, P. K., Lavrukhin, V., Majumdar, S., Noroozi, V., Zhang, Y., Kuchaiev, O., Balam, J., Dovzhenko, Y., Freyberg, K., Shulman, M. D., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1860 · Cited by 88

Self-Attention for Audio Super-Resolution

Rakotonirina, N. C. (2021) · Machine learning for signal processing ... · doi:10.1109/mlsp52302.2021.9596082 · Cited by 37

Investigating Self-supervised Pretraining Frameworks for Pathological Speech Recognition

Violeta, L. P., Huang, W. C., & Toda, T. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10043 · Cited by 40

Novel Speech Recognition Systems Applied to Forensics within Child Exploitation: Wav2vec2.0 vs. Whisper

Vásquez-Correa, J. C., & Álvarez, A. (2023) · Sensors · doi:10.3390/s23041843 · Cited by 42

Wav2Seq: Pre-Training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages

Wu, F., Kim, K., Watanabe, S., Han, K. J., McDonald, R., Weinberger, K. Q., & Artzi, Y. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096988 · Cited by 40

mSilent

Zeng, S., Wan, H., Shi, S., & Wang, W. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3580838 · Cited by 35

Survey on Foundation Models for Prognostics and Health Management in Industrial Cyber-Physical Systems

Liu, R., Zhang, Q., Han, T., Yang, B., Zhang, W., Yin, S., & Zhou, D. (2024) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2024.3425326 · Cited by 36

Exploring Contextual Knowledge-Enhanced Speech Recognition in Air Traffic Control Communication: A Comparative Study

Guo, D., Zhang, S., Zhang, J., Yang, B., & Lin, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3569776 · Cited by 44

Perceptual Loss Based Speech Denoising with an Ensemble of Audio Pattern Recognition and Self-Supervised Models

Kataria, S., Villalba, J., & Dehak, N. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413555 · Cited by 34

When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute

Lei, T. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.602 · Cited by 46

Fastsvc: Fast Cross-Domain Singing Voice Conversion With Feature-Wise Linear Modulation

Liu, S., Cao, Y., Hu, N., Su, D., & Meng, H. M. L. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428161 · Cited by 37

SALM: Speech-Augmented Language Model with in-Context Learning for Speech Recognition and Translation

Chen, Z., Huang, H., Andrusenko, A., Hrinchuk, O., Puvvada, K. C., Li, J., Ghosh, S., Balam, J., & Ginsburg, B. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447553 · Cited by 44

Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study

Chang, X., Yan, B., Choi, K., Jung, J.-W., Lu, Y., Maiti, S., Sharma, R., Shi, J., Tian, J., Watanabe, S., et al. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447929 · Cited by 37

Road Extraction from Remote Sensing Imagery with Spatial Attention Based on Swin Transformer

Zhu, X., Huang, X.-H., Cao, W., Yang, X.-F., Zhou, Y.-F., & Wang, S. (2024) · Remote Sensing · doi:10.3390/rs16071183 · Cited by 41

What Do Self-Supervised Speech Models Know About Words?

Pasad, A., Chien, C.-M., Settle, S., & Livescu, K. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00656 · Cited by 54

Improving RNN Transducer Based ASR with Auxiliary Tasks

Liu, C., Zhang, F., Le, D., Kim, S., Saraf, Y., & Zweig, G. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383548 · Cited by 34

VarArray: Array-Geometry-Agnostic Continuous Speech Separation

Yoshioka, T., Wang, X., Wang, D., Tang, M., Zhu, Z., Chen, Z., & Kanda, N. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746876 · Cited by 34

ConvPatchTrans: A script identification network with global and local semantics deeply integrated

Yang, K., Yi, J., Chen, A., Liu, J., Chen, W., & Jin, Z. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.104916 · Cited by 37

Layer-Wise Fast Adaptation for End-to-End Multi-Accent Speech Recognition

Qian, Y., Gong, X., & Huang, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3198546 · Cited by 34

A CTC Alignment-Based Non-Autoregressive Transformer for End-to-End Automatic Speech Recognition

Fan, R., Chu, W., Chang, P., & Alwan, A. A. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3263789 · Cited by 35

A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition

Ivanko, D., Ryumin, D., & Karpov, A. A. (2023) · Mathematics · doi:10.3390/math11122665 · Cited by 37

Libriheavy: A 50,000 Hours ASR Corpus with Punctuation Casing and Context

Kang, W., Yang, X., Yao, Z., Kuang, F., Yang, Y., Guo, L., Lin, L., & Povey, D. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447120 · Cited by 33

Universal Paralinguistic Speech Representations Using self-Supervised Conformers

Shor, J., Jansen, A., Han, W., Park, D., & Zhang, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747197 · Cited by 34

A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers

Zuluaga-Gómez, J., Prasad, A., Nigmatulina, I., Motlíček, P., & Kleinert, M. (2023) · Aerospace · doi:10.3390/aerospace10050490 · Cited by 38

UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units

Inaguma, H., Popuri, S., Kulikov, I., Chen, P., Wang, C., Chung, Y.-A., Tang, Y., Lee, A., Watanabe, S., & Pino, J. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.872 · Cited by 43

Recent Developments on ESPnet Toolkit Boosted by ConformerPreprint

Guo, P., Boyer, F., Chang, X., Hayashi, T., Higuchi, Y., Inaguma, H., Kamo, N., Li, C., Garcia-Romero, D., Shi, J., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.13956 · Cited by 33

Massively Multilingual ASR: A Lifelong Learning Solution

Li, B., Pang, R., Zhang, Y., Sainath, T. N., Strohman, T., Haghani, P., Zhu, Y., Farris, B., Gaur, N., & Prasad, M. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746594 · Cited by 32

A Multi-Layer Feature Fusion Model Based on Convolution and Attention Mechanisms for Text Classification

Yang, H., Zhang, S., Shen, H., Zhang, G., Deng, X., Xiong, J., Li, F., Wang, J., Zhang, H., & Sheng, S. (2023) · Applied Sciences · doi:10.3390/app13148550 · Cited by 35

TorchAudio 2.1: Advancing Speech Recognition, Self-Supervised Learning, and Audio Processing Components for Pytorch

Hwang, J., Hira, M., Chen, C., Zhang, X., Ni, Z., Sun, G., Ma, P., Huang, R., Pratap, V., Zhang, Y., et al. (2023) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru57964.2023.10389648 · Cited by 33

Less is More: Improved RNN-T Decoding Using Limited Label Context and Path Merging

Prabhavalkar, R., He, Y., Rybach, D., Campbell, S. M., Narayanan, A., Strohman, T., & Sainath, T. N. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414212 · Cited by 32

Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement

Li, A., You, S., Yu, G., Zheng, C., & Li, X. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/582 · Cited by 32

SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection

Kawa, P., Plata, M., & Syga, P. (2022) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom56396.2022.00111 · Cited by 36

LMCodec: A Low Bitrate Speech Codec with Causal Transformer Models

Jenrungrot, T., Chinen, M., Kleijn, W. B., Skoglund, J., Borsos, Z., Zeghidour, N., & Tagliasacchi, M. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095442 · Cited by 32

Transformer-Based Long Distance Fiber Channel Modeling for Optical OFDM Systems

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