The Microsoft 2017 Conversational Speech Recognition System

Deep Residual Learning for Image Recognition

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Long Short-Term Memory

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Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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Backpropagation Applied to Handwritten Zip Code Recognition

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Recurrent neural network based language model

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Convolutional networks for images, speech, and time series

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SRILM - an extensible language modeling toolkit

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A Learning Algorithm for Continually Running Fully Recurrent Neural Networks

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Feature engineering and forecasting via derivative-free optimization and ensemble of sequence-to-sequence networks with applications in renewable energy

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Automatic Lyrics Transcription of Polyphonic Music With Lyrics-Chord Multi-Task Learning

Gao, X., Gupta, C., & Li, H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3190742 · Cited by 36

A Comparison of Online Automatic Speech Recognition Systems and the Nonverbal Responses to Unintelligible SpeechPreprint

Kim, J. Y., Liu, C., Calvo, R. A., McCabe, K., Taylor, S., Schuller, B. W., & Wu, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.12403 · Cited by 33

End-to-End Far-Field Speech Recognition with Unified Dereverberation and Beamforming

Zhang, W., Subramanian, A. S., Chang, X., Watanabe, S., & Qian, Y. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2432 · Cited by 32

Super-Human Performance in Online Low-Latency Recognition of Conversational Speech

Nguyen, T., Stüker, S., & Waibel, A. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1114 · Cited by 37

Persian speech recognition using deep learning

Veisi, H., & Mani, A. H. (2020) · International Journal of Speech Technology · doi:10.1007/s10772-020-09768-x · Cited by 32

Platform for Situated IntelligencePreprint

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“Mm-hm,” “Uh-uh”: are non-lexical conversational sounds deal breakers for the ambient clinical documentation technology?

Tran, B. D., Latif, K., Reynolds, T. L., Park, J., Lafata, J. E., Tai‐Seale, M., & Zheng, K. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad001 · Cited by 31

Cumulative Adaptation for BLSTM Acoustic Models

Kitza, M., Golik, P., Schlüter, R., & Ney, H. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2162 · Cited by 29

End-to-End Spoken Language Understanding: Bootstrapping in Low Resource Scenarios

Bhosale, S., Sheikh, I., Dumpala, S. H., & Kopparapu, S. K. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2366 · Cited by 29

Casting to Corpus: Segmenting and Selecting Spontaneous Dialogue for Tts with a Cnn-lstm Speaker-dependent Breath Detector

Székely, É., Henter, G. E., & Gustafson, J. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683846 · Cited by 27

Bi-directional Lattice Recurrent Neural Networks for Confidence Estimation

Li, Q., Ness, P. M., Ragni, A., & Gales, M. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683488 · Cited by 27

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning

Bock, C., Moor, M., Jutzeler, C. R., & Borgwardt, K. M. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0826-5_2 · Cited by 28

ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition

Pan, J. S., Shapiro, J., Wohlwend, J., Han, K. J., Lei, T., & Ma, T. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2947 · Cited by 30

A novel silent speech recognition approach based on parallel inception convolutional neural network and Mel frequency spectral coefficient

Wu, J., Zhang, Y., Xie, L., Ye, Y., Zhang, X., Liu, S., An, X., Yin, E., & Ming, D. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.971446 · Cited by 26

Session-level Language Modeling for Conversational Speech

Xiong, W., Wu, L., Zhang, J., & Stolcke, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1296 · Cited by 29

Ubiquitous Writer: Robust Text Input for Small Mobile Devices via Acoustic Sensing

Yin, H., Zhou, A., Liu, L., Wang, N., & Ma, H. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2900355 · Cited by 25

Improvements to N-gram Language Model Using Text Generated from Neural Language Model

Suzuki, M., Itoh, N., Nagano, T., Kurata, G., & Thomas, S. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683481 · Cited by 25

SPECPATCH

Guo, H., Wang, Y., Ivanov, N., Xiao, L., & Yan, Q. (2022) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3548606.3560660 · Cited by 25

Audio-driven Neural Gesture Reenactment with Video Motion Graphs

Zhou, Y., Yang, J., Li, D., Saito, J., Aneja, D., & Kalogerakis, E. (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/cvpr52688.2022.00341 · Cited by 25

Side-channel analysis against ANSSI’s protected AES implementation on ARM: end-to-end attacks with multi-task learning

Masure, L., & Strullu, R. (2023) · Journal of Cryptographic Engineering · doi:10.1007/s13389-023-00311-7 · Cited by 25

$\beta$ -Dropout: A Unified Dropout

Liu, L., Luo, Y., Shen, X., Sun, M., & Li, B. (2019) · IEEE Access · doi:10.1109/access.2019.2904881 · Cited by 26

LSTM Language Models for LVCSR in First-Pass Decoding and Lattice-RescoringPreprint

Beck, E., Zhou, W. G., Schlüter, R., & Ney, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01030 · Cited by 25

Machine learning approach to optimal task scheduling in cloud communication

Alsubaei, F. S., Hamed, A. Y., Hassan, M. R., Mohery, M., & Elnahary, M. K. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.01.040 · Cited by 24

Using Chat GPT to Clean Qualitative Interview Transcriptions: A Usability and Feasibility Analysis

Taylor, Z. (2024) · American Journal of Qualitative Research · doi:10.29333/ajqr/14487 · Cited by 24

Beyond SmartNICs: Towards a Fully Programmable Cloud: Invited Paper

Caulfield, A. M., Costa, P., & Ghobadi, M. (2018) · IEEE International Conference on High Performance Switching and Routing (HPSR) · doi:10.1109/hpsr.2018.8850757 · Cited by 24

A Multi-Task Learning Approach for Human Activity Segmentation and Ergonomics Risk Assessment

Parsa, B., & Banerjee, A. G. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00240 · Cited by 28

Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network

An, F.-P., Liu, J.-E., & Bai, L. (2021) · The Visual Computer · doi:10.1007/s00371-020-02033-x · Cited by 24

Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data

Jeon, E. S., Som, A., Shukla, A., Hasanaj, K., Buman, M. P., & Turaga, P. K. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3139038 · Cited by 25

Neuromorphic Hardware Accelerator for SNN Inference based on STT-RAM Crossbar Arrays

Kulkarni, S. R., Kadetotad, D. V., Yin, S., Seo, J.-S., & Rajendran, B. (2019) · 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs46596.2019.8964886 · Cited by 23

An Energy-Efficient Deep Convolutional Neural Network Accelerator Featuring Conditional Computing and Low External Memory Access

Kim, M., & Seo, J.-S. (2020) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2020.3029235 · Cited by 23

Cognitive interaction with virtual assistants: From philosophical foundations to illustrative examples in aeronautics

Bernard, D., & Arnold, A. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.01.010 · Cited by 26

Adversarial Examples Attack and Countermeasure for Speech Recognition System: A Survey

Wang, D., Wang, R., Dong, L., Yan, D., Zhang, X., & Gong, Y. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-9129-7_31 · Cited by 23

A survey on the pipeline evolution of facial capture and tracking for digital humans

Vilchis, C., Pérez-Guerrero, C., Méndez-Ruiz, M., & González-Mendoza, M. (2023) · Multimedia Systems · doi:10.1007/s00530-023-01081-2 · Cited by 22

Hierarchical Multiview Top-k Pooling With Deep-Q-Networks

Li, Z., Su, H., Wu, Y., Zhang, Q., Yuan, C., Gribova, V. V., Filaretov, V. F., & Huang, D.-S. (2023) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2023.3334261 · Cited by 22

Multimodal Driver Interaction with Gesture, Gaze and Speech

Aftab, A. R. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3356093 · Cited by 22

Dialect-Specific Models for Automatic Speech Recognition of African American Vernacular English

Dorn, R. (2019) · doi:10.26615/issn.2603-2821.2019_003 · Cited by 22

Spatial-Temporal Hybrid Neural Network With Computing-in-Memory Architecture

Bai, K., Liu, L., & Yi, Y. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3071956 · Cited by 25

Design Strategies and Applications of Reservoir Computing: Recent Trends and Prospects [Feature]

Bai, K., Thiem, C., Lombardi, J. P., Liang, Y., & Yi, Y. (2023) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2023.3325496 · Cited by 24

A Hybrid Model for Forecasting Traffic Flow: Using Layerwise Structure and Markov Transition Matrix

Zhang, S., Kang, Z., Zhang, Z., Lin, C., Wang, C., & Li, J. (2019) · IEEE Access · doi:10.1109/access.2019.2901118 · Cited by 22

Two-Dimensional Convolutional Recurrent Neural Networks for Speech Activity Detection

Vafeiadis, A., Fanioudakis, E., Potamitis, I., Votis, K., Giakoumis, D., Tzovaras, D. K., Chen, L., & Hamzaoui, R. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1354 · Cited by 21

Protecting the Intellectual Property of Speaker Recognition Model by Black-Box Watermarking in the Frequency Domain

Wang, Y., & Wu, H. (2022) · Symmetry · doi:10.3390/sym14030619 · Cited by 21

Multi-scale graph-transformer network for trajectory prediction of the autonomous vehicles

Singh, D., & Srivastava, R. (2022) · Intelligent Service Robotics · doi:10.1007/s11370-022-00422-w · Cited by 21

GlitchNet: A Glitch Detection and Removal System for SEIS Records Based on Deep Learning

Xu, W., Zhu, Q., & Zhao, L. (2022) · Seismological Research Letters · doi:10.1785/0220210361 · Cited by 22

Evaluating Patients’ Experiences with Healthcare Services: Extracting Domain and Language-Specific Information from Free-Text Narratives

Jacennik, B., Zawadzka-Gosk, E., Moreira, P., & Glinkowski, W. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191610182 · Cited by 21

End-to-End Dereverberation, Beamforming, and Speech Recognition in a Cocktail Party

Zhang, W., Chang, X., Boeddeker, C., Nakatani, T., Watanabe, S., & Qian, Y. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3209942 · Cited by 21

SAPIEN: Affective Virtual Agents Powered by Large Language Models*

Hasan, M., Ozel, C., Potter, S., & Hoque, M. E. (2023) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw59127.2023.10388188 · Cited by 28

Fspen: an Ultra-Lightweight Network for Real Time Speech Enahncment

Yang, L., Liu, W., Meng, R., Lee, G., Baek, S., & Moon, H.-G. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446016 · Cited by 23

Multi-level Binarized LSTM in EEG Classification for Wearable Devices

Nazari, N., Mirsalari, S. A., Sinaei, S., Salehi, M. E., & Daneshtalab, M. (2020) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp50117.2020.00033 · Cited by 21

Hierarchical Phoneme Classification for Improved Speech Recognition

Oh, D., Park, J., Kim, J., & Jang, G. (2021) · Applied Sciences · doi:10.3390/app11010428 · Cited by 21

Double Forward Propagation for Memorized Batch Normalization

Guo, Y. F., Wu, Q., Deng, C., Chen, J., & Tan, M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11717 · Cited by 22

Dual supervised learning for non-native speech recognition

Radzikowski, K., Nowak, R. M., Wang, L., & Yoshie, O. (2019) · EURASIP Journal on Audio Speech and Music Processing · doi:10.1186/s13636-018-0146-4 · Cited by 21

Neural Acoustic-Phonetic Approach for Speaker Verification With Phonetic Attention Mask

Liu, T., Das, R. K., Lee, K. A., & Li, H. (2022) · IEEE Signal Processing Letters · doi:10.1109/lsp.2022.3143036 · Cited by 19

Variation-Tolerant and Low R-Ratio Compute-in-Memory ReRAM Macro With Capacitive Ternary MAC Operation

Jeong, S., Kim, J., Jeong, M., & Lee, Y. (2022) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2022.3165352 · Cited by 19

Acoustic Modeling for Overlapping Speech Recognition: Jhu Chime-5 Challenge System

Manohar, V., Chen, S.-J., Wang, Z., Fujita, Y., Watanabe, S., & Khudanpur, S. P. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682556 · Cited by 19

A Unified Framework for Neural Speech Separation and Extraction

Ochiai, T., Delcroix, M., Kinoshita, K., Ogawa, A., & Nakatani, T. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683448 · Cited by 19

Machine Learning to Identify Dynamic Properties of Railway Track Components

Kaewunruen, S., Sresakoolchai, J., & Stittle, H. (2022) · International Journal of Structural Stability and Dynamics · doi:10.1142/s0219455422501097 · Cited by 18

AISpeech-SJTU ASR System for the Accented English Speech Recognition Challenge

Tan, T., Lu, Y., Ma, R., Zhu, S., Guo, J., & Qian, Y. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414471 · Cited by 15

Convolutional Recurrent Neural Network-Based Event Detection in Tunnels Using Multiple Microphones

Kim, N. K., Jeon, K. M., & Kim, H. K. (2019) · Sensors · doi:10.3390/s19122695 · Cited by 18

DiReCtX: Dynamic Resource-Aware CNN Reconfiguration Framework for Real-Time Mobile Applications

Xu, Z., Yu, F., Qin, Z., Liu, C., & Chen, X. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2020.2995813 · Cited by 17

Extracting Cause of Death from Verbal Autopsy with Deep Learning interpretable methods

Blanco, A., Perez, A., Casillas, A., & Cobos, D. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3005769 · Cited by 17

From Quantitative Analysis to Synthesis of Efficient Binary Neural Networks

Stadtmann, T., Latotzke, C., & Gemmeke, T. (2020) · International Conference on Machine Learning and Applications · doi:10.1109/icmla51294.2020.00024 · Cited by 16

Can Machine Learning Facilitate Remote Pair Programming? Challenges, Insights & Implications

Robe, P., Kuttal, S. K., Zhang, Y., & Bellamy, R. (2020) · IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) · doi:10.1109/vl/hcc50065.2020.9127250 · Cited by 16

Deep learning for photoacoustic imaging: a surveyPreprint

Yang, C., Lan, H., Gao, F., & Gao, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.04221 · Cited by 16

Dysarthric Speech Recognition Using Pseudo-Labeling, Self-Supervised Feature Learning, and a Joint Multi-Task Learning Approach

Takashima, R., Sawa, Y., Aihara, R., Takiguchi, T., & Imai, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3374874 · Cited by 15

Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models

Gao, X., & Chen, N. F. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832137 · Cited by 15

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Petroșanu, D.-M. (2019) · Processes · doi:10.3390/pr7050310 · Cited by 15

BlockFedML: Blockchained Federated Machine Learning Systems

Wang, S. (2019) · International Conference on Intelligent Computing, Automation and Systems (ICICAS) · doi:10.1109/icicas48597.2019.00162 · Cited by 15

Analysis and Detection of Lung Sounds Anomalies Based on NMA-RNN

Manzoor, A., Pan, Q., Khan, H. J., Siddeeq, S., Bhatti, H. M. A., & Wedagu, M. A. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313197 · Cited by 16

Actively listening twin robots for long-duration conversation with the elderly

Nishio, T., Yoshikawa, Y., Iio, T., Chiba, M., Asami, T., Isoda, Y., & Ishiguro, H. (2021) · ROBOMECH Journal · doi:10.1186/s40648-021-00205-5 · Cited by 15

LipSound: Neural Mel-Spectrogram Reconstruction for Lip Reading

Qu, L., Weber, C., & Wermter, S. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-1393 · Cited by 15

A Comprehensive Evaluation of Incremental Speech Recognition and Diarization for Conversational AI

Addlesee, A., Yu, Y., & Eshghi, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.312 · Cited by 15

Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain–computer interface

Ko, W., Jeon, E., Yoon, J. S., & Suk, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-08490-9 · Cited by 14

A Review of Natural-Language-Instructed Robot Execution Systems

Liu, R., Guo, Y., Jin, R., & Zhang, X. (2024) · AI · doi:10.3390/ai5030048 · Cited by 15

Priority Determination to Apply Artificial Intelligence Technology in Military Intelligence Areas

Cho, S., Shin, W., 김능회, Jeong, J., & In, H. P. (2020) · Electronics · doi:10.3390/electronics9122187 · Cited by 14

A Voice Communication-Augmented Simulation Framework for Aircraft Trajectory Simulation

Wang, Y., Pang, Y., Gorceski, S., Kostiuk, P. F., Mohen, M. T., Menon, P. K., & Liu, Y. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3068476 · Cited by 14

Time Delay Recurrent Neural Network for Speech Recognition

Liu, B.-J., Zhang, W., Xu, X., & Chen, D. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1229/1/012078 · Cited by 14

Multilingual Phonetic Dataset for Low Resource Speech Recognition

Li, X., Mortensen, D. R., Metze, F., & Black, A. W. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413720 · Cited by 14

End-to-End Lip-Reading Open Cloud-Based Speech Architecture

Jeon, S., & Kim, M. S. (2022) · Sensors · doi:10.3390/s22082938 · Cited by 13

A Variable Batch Size Strategy for Large Scale Distributed DNN Training

Hu, Z., Xiao, J., Tian, Z., Zhang, X., Zhu, H., Yao, C., Sun, N., & Tan, G. (2019) · IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) · doi:10.1109/ispa-bdcloud-sustaincom-socialcom48970.2019.00074 · Cited by 14

Image classification algorithm based on stacked sparse coding deep learning model-optimized kernel function nonnegative sparse representation

An, F. (2020) · Soft Computing · doi:10.1007/s00500-020-04989-3 · Cited by 13

Building a speech recognition system with privacy identification information based on Google Voice for social robots

Lin, P., Yankson, B., Chauhan, V., & Tsukada, M. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04487-3 · Cited by 13

Joint Optimization of Quantization and Structured Sparsity for Compressed Deep Neural Networks

Srivastava, G., Kadetotad, D. V., Yin, S., Berisha, V., Chakrabarti, C., & Seo, J.-S. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682791 · Cited by 13

Data Augmentation Based on Vowel Stretch for Improving Children's Speech Recognition

Nagano, T., Fukuda, T., Suzuki, M., & Kurata, G. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003741 · Cited by 13

Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation

Taran, O., Rezaeifar, S., Holotyak, T., & Voloshynovskiy, S. (2020) · EURASIP Journal on Information Security · doi:10.1186/s13635-020-00106-x · Cited by 15

Use of Machine Learning by Non-Expert DHH People: Technological Understanding and Sound Perception

Nakao, Y., & Sugano, Y. (2020) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3419249.3420157 · Cited by 15

Efficient Neural Network Approaches for Leather Defect ClassificationPreprint

Liong, S., Gan, Y. S., Liu, K., Binh, T. Q., Le, C. T., Wu, C.-A., Yang, C., & Huang, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06446 · Cited by 13

Fast and Robust Unsupervised Contextual Biasing for Speech RecognitionPreprint

Kang, Y. M., & Zhou, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.01677 · Cited by 13

Noise-Robust Multimodal Audio-Visual Speech Recognition System for Speech-Based Interaction Applications

Jeon, S., & Kim, M. S. (2022) · Sensors · doi:10.3390/s22207738 · Cited by 12

Machine Learning in Computer Aided Engineering

Montáns, F. J., Cueto, E., & Bathe, K. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-36644-4_1 · Cited by 13

Developing a negative speech emotion recognition model for safety systems using deep learning

Jena, S., Basak, S., Agrawal, H., Saini, B., Gite, S., Kotecha, K. V., & Alfarhood, S. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01090-0 · Cited by 14

English Broadcast News Speech Recognition by Humans and Machines

Thomas, S., Suzuki, M., Huang, Y., Kurata, G., Tüske, Z., Saon, G., Kingsbury, B. E. D., Picheny, M., Dibert, T., Kaiser-Schatzlein, A., et al. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683211 · Cited by 12

Unsupervised Learning for Log Data Analysis Based on Behavior and Attribute Features

Wang, X., Wang, D., Zhang, Y., Jin, L., & Song, M. N. (2019) · International Conference on Artificial Intelligence and Computer Science (AICS) · doi:10.1145/3349341.3349460 · Cited by 13

Phoneme classification in reconstructed phase space with convolutional neural networks

Wesley, R. J., Khan, A. N., & Shahina, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.05.002 · Cited by 12

CHF Detection with LSTM Neural Network

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