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

CoNeTTE: An Efficient Audio Captioning System Leveraging Multiple Datasets With Task Embedding

Labbé, É., Pellegrini, T., & Pinquier, J. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3430813 · Cited by 14

GTSO: Gradient tangent search optimization enabled voice transformer with speech intelligibility for aphasia

Ranjith, R., & Chandrasekar, A. (2023) · Computer Speech & Language · doi:10.1016/j.csl.2023.101568 · Cited by 14

A Semi-Supervised Complementary Joint Training Approach for Low-Resource Speech Recognition

Du, Y., Zhang, J., Fang, X., Wu, M., & Yang, Z. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3313434 · Cited by 14

Direct Speech Translation for Automatic Subtitling

Papi, S., Gaido, M., Karakanta, A., Cettolo, M., Negri, M., & Turchi, M. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00607 · Cited by 14

ProbSparse Attention-Based Fault Diagnosis for Industrial Robots Under Different Working Conditions

Wang, Y., He, Y., Kang, B., Liu, J., & Sun, C. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3374323 · Cited by 14

Stateful Conformer with Cache-Based Inference for Streaming Automatic Speech Recognition

Noroozi, V., Majumdar, S., Kumar, A., Balam, J., & Ginsburg, B. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446861 · Cited by 14

Retrieval Augmented End-to-End Spoken Dialog Models

Wang, M., Izhak Shafran, J., Soltau, H., Han, W., Cao, Y. J., Yu, D., & Shafey, L. E. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447448 · Cited by 14

Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition

Puvvada, K. C., Koluguri, N. R., Dhawan, K., Balam, J., & Ginsburg, B. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446998 · Cited by 18

Maskmark: Robust Neuralwatermarking for Real and Synthetic Speech

O’Reilly, P., Jin, Z., Su, J., & Pardo, B. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447253 · Cited by 14

Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture

Yang, G., He, M., Niu, S., Wang, R., Yue, Y., Qian, S., Wu, S., Du, J., & Lee, C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446661 · Cited by 14

Multilingual Audio-Visual Speech Recognition with Hybrid CTC/RNN-T Fast Conformer

Burchi, M., Puvvada, K. C., Balam, J., Ginsburg, B., & Timofte, R. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10445891 · Cited by 15

Combining wav2vec 2.0 Fine-Tuning and ConLearnNet for Speech Emotion Recognition

Sun, C., Zhou, Y., Huang, X. M., Yang, J., & Hou, X. (2024) · Electronics · doi:10.3390/electronics13061103 · Cited by 14

Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder

Chen, Z., Liu, S., Liu, S., Chang, Y., Chen, J., Feng, G., & He, S. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111922 · Cited by 14

MELDER: The Design and Evaluation of a Real-time Silent Speech Recognizer for Mobile Devices

Pandey, L., & Arif, A. S. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642348 · Cited by 15

Learning Robust Global-Local Representation From EEG for Neural Epilepsy Detection

Zhou, X., Liu, C., Yang, R., Zhang, L., Zhai, L., Jia, Z., & Liu, Y. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3406289 · Cited by 14

Recognition Model for Tea Grading and Counting Based on the Improved YOLOv8n

Xia, Y., Wang, Z., Cao, Z., Chen, Y., Li, L., Chen, L., Zhang, S., Wang, C., Li, H., & Wang, B. (2024) · Agronomy · doi:10.3390/agronomy14061251 · Cited by 16

DualDomain-AttenNet: Synergizing time–frequency analysis and attention mechanisms for Motor Imagery BCI enhancement

Liu, C., & Huang, P.-T. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102697 · Cited by 15

EST transformer: enhanced spatiotemporal representation learning for time series anomaly detection

Gao, Y., Su, R., Ben, X., & Chen, L. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00918-8 · Cited by 14

Deformable Graph Transformer

Park, J., Yun, S., Park, H., Kang, J., Jeong, J., Kim, K.-M., Ha, J.-W., & Kim, H. J. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3550281 · Cited by 21

USEF-TSE: Universal Speaker Embedding Free Target Speaker Extraction

Zeng, B., & Li, M. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3572756 · Cited by 17

Cross-Referencing Self-Training Network for Sound Event Detection in Audio Mixtures

Park, S., Han, D. K., & Elhilali, M. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3178591 · Cited by 14

Streaming End-to-End ASR Based on Blockwise Non-Autoregressive Models

Wang, T., Fujita, Y., Chang, X., & Watanabe, S. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1556 · Cited by 14

Enabling On-Device Training of Speech Recognition Models With Federated Dropout

Guliani, D., Zhou, L., Ryu, C.-W., Yang, T.-J., Zhang, H., Xiao, Y., Beaufays, F., & Motta, G. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746226 · Cited by 14

Conformer-Based Self-Supervised Learning For Non-Speech Audio Tasks

Srivastava, S., Wang, Y., Tjandra, A., Kumar, A., Liu, C., Singh, K., & Saraf, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746490 · Cited by 16

Cross-Attention Conformer for Context Modeling in Speech Enhancement for ASR

Narayanan, A., Chiu, C., O'Malley, T., Wang, Q., & He, Y. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688173 · Cited by 14

Tiny-Sepformer: A Tiny Time-Domain Transformer Network For Speech Separation

Luo, J., Wang, J., Cheng, N., Xiao, E., Zhang, X., & Xiao, J. Z. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-66 · Cited by 14

Discriminative speaker embedding with serialized multi-layer multi-head attention

Zhu, H., Lee, K. A., & Li, H. (2022) · Speech Communication · doi:10.1016/j.specom.2022.09.003 · Cited by 14

Tackling the Cocktail Fork Problem for Separation and Transcription of Real-World Soundtracks

Petermann, D., Wichern, G., Subramanian, A. S., Wang, Z.-Q., & Roux, J. L. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3290428 · Cited by 14

LegoNN: Building Modular Encoder-Decoder Models

Dalmia, S., Okhonko, D., Lewis, M., Edunov, S., Watanabe, S., Metze, F., Zettlemoyer, L., & Mohamed, A. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3296019 · Cited by 19

Transformer-Conformer Ensemble for Crash Prediction Using Connected Vehicle Trajectory Data

Islam, Z., Abdel-Aty, M. A., & Anik, B. M. T. H. (2023) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2023.3339016 · Cited by 16

Learning the Beauty in Songs: Neural Singing Voice Beautifier

Liu, J., Li, C., Ren, Y., Zhu, Z., & Zhao, Z. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.549 · Cited by 14

Efficient conformer-based speech recognition with linear attentionPreprint

Li, S., Xu, M., & Zhang, X.-L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.06865 · Cited by 20

Continual-wav2vec2: an Application of Continual Learning for Self-Supervised Automatic Speech Recognition.Preprint

Kessler, S., Thomas, B., & Karout, S. A. (2021) · arXiv (Cornell University) · Cited by 15

Transformer-based auto-encoder with combined multi-head-attention for industrial soft-sensor modeling

Li, Y., Gao, S., & Zhao, W. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111681 · Cited by 13

Analogue speech recognition based on physical computing

Zolfagharinejad, M., Büchel, J., Cassola, L., Kinge, S., Sarwat, S. G., Sebastian, A., & van der Wiel, W. G. (2025) · Nature · doi:10.1038/s41586-025-09501-1 · Cited by 13

Automated classification of cardiology diagnoses based on textual medical reports

Pedrosa, J. A. O., Oliveira, D. M., Meira, W., & Ribeiro, A. L. P. (2021) · Journal of Information and Data Management · doi:10.5753/jidm.2021.1940 · Cited by 14

CTC Variations Through New WFST Topologies

Laptev, A., Majumdar, S., & Ginsburg, B. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10854 · Cited by 13

Multi-Modal Pre-Training for Automated Speech Recognition

Chan, D. M., Ghosh, S., Chakrabarty, D., & Hoffmeister, B. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746449 · Cited by 13

Contrastive Siamese Network for Semi-Supervised Speech Recognition

Khorram, S., Kim, J., Tripathi, A., Han, L., Zhang, Q., & Sak, H. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747355 · Cited by 13

Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden Intermediates

Inaguma, H., Dalmia, S., Yan, B., & Watanabe, S. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9687894 · Cited by 13

Application of virtual human sign language translation based on speech recognition

Li, X., Yang, S., & Haiming, G. (2023) · Speech Communication · doi:10.1016/j.specom.2023.06.001 · Cited by 13

HRCTNet: a hybrid network with high-resolution representation for object detection in UAV image

Xing, W., Cui, Z., & Qi, J. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01076-6 · Cited by 13

Three-stage hybrid neural beamformer for multi-channel speech enhancement

Kuang, K., Yang, F., Li, J., & Yang, J. J. (2023) · The Journal of the Acoustical Society of America · doi:10.1121/10.0019802 · Cited by 13

ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit

Yan, B., Shi, J., Tang, Y., Inaguma, H., Peng, Y., Dalmia, S., Polák, P., Fernandes, P., Berrebbi, D., Hayashi, T., et al. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.38 · Cited by 13

MIR-GAN: Refining Frame-Level Modality-Invariant Representations with Adversarial Network for Audio-Visual Speech Recognition

Hu, Y., Chen, C., Li, R., Zou, H., & Chng, E. S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.649 · Cited by 13

Frontier Research on Low-Resource Speech Recognition Technology

Slam, W., Li, Y., & Urouvas, N. (2023) · Sensors · doi:10.3390/s23229096 · Cited by 13

Pronunciation error detection model based on feature fusion

Zhu, C., Wumaier, A., Wei, D., Fan, Z., Yang, J., Yu, H., Kadeer, Z., & Wang, L. (2023) · Speech Communication · doi:10.1016/j.specom.2023.103009 · Cited by 13

Unified Model for Code-Switching Speech Recognition and Language Identification Based on Concatenated Tokenizer

Dhawan, K., Rekesh, K., & Ginsburg, B. (2023) · Workshop on Computational Approaches to Code Switching · doi:10.18653/v1/2023.calcs-1.7 · Cited by 13

MECOS: A bilingual Manipuri–English spontaneous code-switching speech corpus for automatic speech recognition

Singh, N. K., Chanu, Y. J., & Pangsatabam, H. (2024) · Computer Speech & Language · doi:10.1016/j.csl.2024.101627 · Cited by 13

PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station

Yin, C., Miao, X., Chen, J., Jiang, H., Yang, J., Zhou, Y., Wu, M., & Chen, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3369856 · Cited by 15

Improving End-to-End Models for Children’s Speech Recognition

Patel, T. B., & Scharenborg, O. (2024) · Applied Sciences · doi:10.3390/app14062353 · Cited by 13

Contextualized Automatic Speech Recognition With Attention-Based Bias Phrase Boosted Beam Search

Sudo, Y., Shakeel, M., Fukumoto, Y., Peng, Y., & Watanabe, S. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447782 · Cited by 13

Leveraging Large Language Models for Exploiting ASR Uncertainty

Dighe, P., Su, Y., Zheng, S., Liu, Y., Garg, V., Niu, X., & Tewfik, A. H. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446132 · Cited by 19

SlideSpeech: A Large Scale Slide-Enriched Audio-Visual Corpus

Wang, H., Yu, F., Shi, X., Wang, Y., Zhang, S. S., & Li, M. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10448079 · Cited by 14

MMGER: Multi-Modal and Multi-Granularity Generative Error Correction With LLM for Joint Accent and Speech Recognition

Mu, B., Wan, X., Zheng, N., Zhou, H., & Xie, L. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3432275 · Cited by 15

Implementation of a Whisper Architecture-Based Turkish Automatic Speech Recognition (ASR) System and Evaluation of the Effect of Fine-Tuning with a Low-Rank Adaptation (LoRA) Adapter on Its Performance

Polat, H., Turan, A. K., Koçak, C., & Ulaş, H. B. (2024) · Electronics · doi:10.3390/electronics13214227 · Cited by 18

Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap

Yang, G., Yu, F., Ma, Z., Du, Z., Gao, Z., Zhang, S. S., & Chen, X. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10889894 · Cited by 13

LLM based Text Generation for Improved Low-resource Speech Recognition Models

Nagano, T., Kurata, G., Thomas, S., Kuo, H.-K. J., Bolaños, D., Jung, H., & Saon, G. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888566 · Cited by 13

Layer Pruning on Demand with Intermediate CTC

Lee, J., Kang, J., & Watanabe, S. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1171 · Cited by 13

Decoupling Recognition and Transcription in Mandarin ASR

Yuan, J., Cai, X., Gao, D., Zheng, R., Huang, L., & Church, K. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688053 · Cited by 13

Improving Mandarin End-to-End Speech Recognition With Word N-Gram Language Model

Tian, J., Yu, J., Weng, C., Zou, Y., & Yu, D. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3154241 · Cited by 13

Deep Learning Enabled High-Performance Speech Command Recognition on Graphene Flexible Microphones

Zhang, X.-Y., Liu, H., Ma, X., Wang, Z., Li, G., Han, L., Sun, K., Yang, Q., Ji, S., Yu, D., et al. (2022) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.2c00125 · Cited by 13

Note-level Automatic Guitar Transcription Using Attention Mechanism

Kim, S., Hayashi, T., & Toda, T. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909659 · Cited by 13

MFCCA:Multi-Frame Cross-Channel Attention for Multi-Speaker ASR in Multi-Party Meeting Scenario

Yu, F., Zhang, S. S., Guo, P., Liang, Y., Du, Z., Lin, Y., & Xie, L. (2023) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt54892.2023.10022715 · Cited by 13

Improving Automatic Speech Recognition for Non-native English with Transfer Learning and Language Model Decoding

Sullivan, P., Shibano, T., & Abdul-Mageed, M. (2022) · Signals and communication technology · doi:10.1007/978-3-031-11035-1_2 · Cited by 13

Confidence Score Based Speaker Adaptation of Conformer Speech Recognition Systems

Deng, J., Xie, X., Wang, T., Cui, M., Xue, B., Jin, Z., Li, G., Hu, S., & Liu, X. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3250842 · Cited by 13

Improving CTC-Based ASR Models With Gated Interlayer Collaboration

Yang, Y., Li, Y., & Du, B. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094820 · Cited by 13

SoftCorrect: Error Correction with Soft Detection for Automatic Speech Recognition

Leng, Y., Tan, X., Liu, W., Song, K., Wang, R., Li, X., Qin, T., Lin, E., & Liu, T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26531 · Cited by 18

Cross-Modal Global Interaction and Local Alignment for Audio-Visual Speech Recognition

Hu, Y., Li, R., Chen, C., Zou, H., Zhu, Q., & Chng, E. S. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/564 · Cited by 13

A New Benchmark of Aphasia Speech Recognition and Detection Based on E-Branchformer and Multi-task Learning

Tang, J., Chen, W., Chang, X., Watanabe, S., & MacWhinney, B. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-2191 · Cited by 19

ESAformer: Enhanced Self-Attention for Automatic Speech Recognition

Li, J., Duan, Z., Li, S., Yu, X., & Yang, G. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3358754 · Cited by 13

Unsupervised Learning of Disentangled Speech Content and Style Representation

Tjandra, A., Pang, R., Zhang, Y., & Karita, S. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1936 · Cited by 19

Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition

Laptev, A., Andrusenko, A., Podluzhny, I., Mitrofanov, A., Medennikov, I., & Matveev, Y. N. (2021) · Sensors · doi:10.3390/s21093063 · Cited by 13

Exploring Targeted Universal Adversarial Perturbations to End-to-End ASR Models

Lu, Z., Han, W., Zhang, Y., & Cao, L. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1668 · Cited by 13

Multi-Branch Convolutional Macaron net for Sound Event Detection

Chan, T. K., & Chin, C. S. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3110649 · Cited by 13

Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization

Yan, B., Zhang, C., Yu, M., Zhang, S.-X., Dalmia, S., Berrebbi, D., Weng, C., Watanabe, S., & Yu, D. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747537 · Cited by 13

The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and MethodsPreprint

Shi, X. J., Yu, F., Lu, Y., Liang, Y., Feng, Q., Wang, D., Qian, Y., & Xie, L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.10233 · Cited by 13

Attentive Temporal Pooling for Conformer-Based Streaming Language Identification in Long-Form Speech

Wang, Q., Yang, Y. S., Pelecanos, J. W., Huang, Y., & Moreno, I. L. (2022) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2022-36 · Cited by 12

Listen, Know and Spell: Knowledge-Infused Subword Modeling for Improving ASR Performance of OOV Named Entities

Das, N., Sunkara, M., Bekal, D., Chau, D. H., Bodapati, S., & Kirchhoff, K. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746748 · Cited by 12

VCVTS: Multi-Speaker Video-to-Speech Synthesis Via Cross-Modal Knowledge Transfer from Voice Conversion

Wang, D., Yang, S., Su, D., Liu, X., Yu, D., & Meng, H. M. L. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747427 · Cited by 12

Ultra-Low-Bitrate Speech Coding with Pretrained Transformers

Siahkoohi, A., Chinen, M., Denton, T., Kleijn, W. B., & Skoglund, J. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-10988 · Cited by 12

CMU’s IWSLT 2022 Dialect Speech Translation System

Yan, B., Fernandes, P., Dalmia, S., Shi, J., Peng, Y., Berrebbi, D., Wang, X., Neubig, G., & Watanabe, S. (2022) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2022.iwslt-1.27 · Cited by 12

Emphasizing unseen words: New vocabulary acquisition for end-to-end speech recognition

Qu, L., Weber, C., & Wermter, S. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.01.027 · Cited by 12

Speaker Adaptive Text-to-Speech With Timbre-Normalized Vector-Quantized Feature

Du, C., Guo, Y., Chen, X., & Yu, K. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3308374 · Cited by 13

IAN-BERT: Combining Post-trained BERT with Interactive Attention Network for Aspect-Based Sentiment Analysis

Verma, S., Kumar, A., & Sharan, A. (2023) · SN Computer Science · doi:10.1007/s42979-023-02229-7 · Cited by 12

Task-Oriented Semantic Communications for Speech Transmission

Weng, Z., Qin, Z., & Tao, X. (2023) · IEEE Vehicular Technology Conference · doi:10.1109/vtc2023-fall60731.2023.10333632 · Cited by 12

Brain-Inspired Recognition System Based on Multimodal In-Memory Computing Framework for Edge AI

Xiao, H., Xie, D., Hu, X., Zhou, Y., Zhou, G., & Duan, S. (2023) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2023.3337532 · Cited by 12

AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR

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Exploring Native and Non-Native English Child Speech Recognition With Whisper

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Efficient Personal Voice Activity Detection with Wake Word Reference Speech

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TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare

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Dual Script E2E Framework for Multilingual and Code-Switching ASR

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VHF Speech Enhancement Based on Transformer

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Self-Critical Sequence Training for Automatic Speech Recognition

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Enhancing Speech Recognition: A Fusion of Transformers with Convolutional Context

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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

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

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