The Microsoft 2017 Conversational Speech Recognition System

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

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

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891

Framewise phoneme classification with bidirectional LSTM and other neural network architectures

Graves, A., & Schmidhuber, J. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.042 · Cited by 6,152

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390

Convolutional networks for images, speech, and time series

LeCun, Y., & Bengio, Y. (1998) · HAL · doi:10.5555/303568.303704 · Cited by 4,646

SRILM - an extensible language modeling toolkit

Stolcke, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-303 · Cited by 4,385

A Learning Algorithm for Continually Running Fully Recurrent Neural Networks

Williams, R. J., & Zipser, D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.2.270 · Cited by 4,369

Broken Access: On the Challenges of Screen Reader Assisted Two-Factor and Passwordless Authentication

Akanda, M. M. R. R., Mahdad, A. T., & Saxena, N. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3696410.3714579 · Cited by 3

Machine Discriminating: Automated Speech Recognition Biases in Refugee Interviews

Bircan, T., & Ceylan, D. (2024) · Journal of Immigrant & Refugee Studies · doi:10.1080/15562948.2024.2355613 · Cited by 4

Deep Learning Application in Security and Privacy – Theory and Practice: A Position Paper

Meister, J. A., Akram, R. N., & Markantonakis, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20074-9_10 · Cited by 3

Building Next-Generation AI systems: Co-Optimization of Algorithms, Architectures, and Nanoscale Memristive Devices

Rajendran, B., Sebastian, A., & Eleftheriou, E. S. (2019) · IEEE International Memory Workshop (IMW) · doi:10.1109/imw.2019.8739740 · Cited by 3

Iteration number-based hierarchical gradient aggregation for distributed deep learning

Xiao, D., Li, X., Zhou, J., Du, Y., & Wu, W. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04083-x · Cited by 3

Secure Intelligence and Prediction in Crisp Business Using Artificial Intelligence Techniques

Priya, J. S., Aravinth, S., Ruban, S., Jaganraj, D., & Gokulakrishnan, C. K. (2023) · International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) · doi:10.1109/vitecon58111.2023.10157191 · Cited by 3

Static and Dynamic State Predictions for Acoustic Model Combination

Kumar, K., & Gong, Y. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683436 · Cited by 3

Improving ASR Robustness to Perturbed Speech Using Cycle-consistent Generative Adversarial Networks

Dumpala, S. H., Sheikh, I., Chakraborty, R., & Kopparapu, S. K. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683793 · Cited by 3

Neural Networks and Advanced Algorithms for Intelligent Monitoring in Industry

Schmid, P. A. E., Steinecker, A., Sun, J., & Knapp, H. F. (2019) · Lecture notes in mobility · doi:10.1007/978-3-030-14156-1_14 · Cited by 3

Effect Analysis of Low-Level Hardware Faults on Neural Networks using Emulated Inference

Bahnsen, F. H., Klebe, V., & Fey, G. (2021) · International Conference on Modern Circuits and Systems Technologies · doi:10.1109/mocast52088.2021.9493350 · Cited by 3

On Architectures and Training for Raw Waveform Feature Extraction in ASR

Vieting, P., Luscher, C., Michel, W., Schlüter, R., & Ney, H. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688123 · Cited by 3

Bearing Intelligent Fault Diagnosis Under Complex Working Condition Based on SK-ES-CNN

Zheng-ping, L., Liu, K., & Xiao, L. (2021) · Global Reliability and Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-nanjing52125.2021.9613125 · Cited by 3

Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models

Ogawa, A., Tawara, N., Delcroix, M., & Araki, S. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747745 · Cited by 3

Convolutional Recurrent Neural Networks for Computer Network Analysis

Nowak, J., Korytkowski, M., & Scherer, R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30490-4_59 · Cited by 3

Design the Future Activities (DFA): A Pedagogical Content Knowledge Framework in Engineering Design Education

Ali, H., & Maynard, A. (2021) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--36924 · Cited by 4

M3D-GAN: Multi-Modal Multi-Domain Translation with Universal AttentionPreprint

Ma, S., McDuff, D., & Song, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.04378 · Cited by 4

Improving N-gram Language Models with Pre-trained Deep TransformerPreprint

Wang, Y., Huang, H., Liu, Z., Pang, Y., Wang, Y., Zhai, C., & Peng, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10235 · Cited by 5

Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech RecognitionPreprint

Segbroeck, M. V., Mallidi, S. H., King, B., Chen, I.-F., Chadha, G., & Maas, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00131 · Cited by 3

Temporal Extraction and Retrieval

Alonso, O., & Spaniol, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674137 · Cited by 2

Neural Information Retrieval

Tonellotto, N. (2024) · ACM eBooks · doi:10.1145/3674127.3674130 · Cited by 3

Design of a DNN-based operator on edge device for keyword spotting

Wei, C. K., & Nugroho, H. (2025) · Advances in Computational Intelligence · doi:10.1007/s43674-025-00080-2 · Cited by 2

Pedestrian trajectory prediction: a literature review and current trends

Zaier, M., Wannous, H., Drira, H., & Boonært, J. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11663-y · Cited by 4

Research on NLP for RE at CNR-ISTI: a Report.

Gnesi, S., & Ferrari, A. (2018) · REFSQ Workshops · Cited by 2

On Reducing the Effect of Speaker Overlap for Chime-5

Zorilă, C., & Doddipatla, R. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683391 · Cited by 2

Mitigating Adversarial Attacks Based on Denoising & Reconstruction With Finance Authentication System Case Study

Wang, J., Hu, Y., Qi, Y., Peng, Z., & Zhou, C. (2021) · IEEE Transactions on Computers · doi:10.1109/tc.2021.3066614 · Cited by 2

Professional Search

Verberne, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674141 · Cited by 5

Evaluation of IR Systems

Ferro, N., & Maistro, M. (2024) · ACM eBooks · doi:10.1145/3674127.3674132 · Cited by 3

Privacy in Information Retrieval

Soboroff, I. M. (2024) · ACM eBooks · doi:10.1145/3674127.3674139 · Cited by 2

Advancements in Voice Assistants: A Study of Speech Recognition and Emotional Intelligence

Jee, N., Kumar, S., Patel, R. R., Mandal, R., Singh, R. K., & Vardhan, H. (2024) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart63812.2024.10882573 · Cited by 2

Closing the Loop on Speech to Music Translation: Automatically Generating Synthetic Percussive Sequences on the Mridangam from Konnakol

Krishnan, G., Drabek, J., Anantapadmanabhan, A., Ganguli, K. K., & Guedes, C. (2025) · IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) · doi:10.1109/icasspw65056.2025.11011256 · Cited by 2

A Fleet-Based Machine Learning Approach for Automatic Detection of Deviations between Measurements and Reality

Pfeiffer, J., Wolf, P., & Pereira, R. (2019) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2019.8813858 · Cited by 2

Application of Progressive Neural Networks for Multi-Stream Wfst Combination in One-Pass Decoding

Xu, S., & Fosler‐Lussier, E. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8462044 · Cited by 2

An Investigation of Word Embeddings with Deep Bidirectional LSTM for Sentence Unit Detection in Automatic Speech Transcription

Ho, T.-N., Can, D.-C., & Chng, E. S. (2018) · International Conference on Asian Language Processing · doi:10.1109/ialp.2018.8629114 · Cited by 2

Transfer Learning for Low Resource Spoken Language Understanding without Speech-to-Text

Bhosale, S., Sheikh, I., Dumpala, S. H., & Kopparapu, S. K. (2019) · IEEE Bombay Section Signature Conference (IBSSC) · doi:10.1109/ibssc47189.2019.8973067 · Cited by 2

Spoken Dialogue-based Remote Control of Educational Mobile Robot with Mecanum Wheels

Chivarov, N., Chikurtev, D., Pleva, M., Ondáš, S., Juhár, J., & Liao, Y. (2019) · International Conference on Emerging eLearning Technologies and Applications (ICETA) · doi:10.1109/iceta48886.2019.9040010 · Cited by 2

A Brazilian Portuguese Real-Time Voice Recognition to deal with sensitive data

Pinna, F. C. D. A., Néto, J. C., & Ruggiero, W. V. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048689 · Cited by 2

Provable Consent for Voice User Interfaces

Sigg, S., Nguyen, L. N., Zarazaga, P. P., & Bäckström, T. (2020) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops48775.2020.9156182 · Cited by 2

Development of a Novel System for Speaker Verification

Ullah, F., Israr, M. B., Jan, A., Ahmad, A. M., Dullah, I., & Ullah, F. (2020) · International Conference on Intelligent Engineering and Management (ICIEM) · doi:10.1109/iciem48762.2020.9160019 · Cited by 2

Influence of Emotional Speech on Continuous Speech Recognition

Žgank, A., & Maučec, M. S. (2020) · ELEKTRO · doi:10.1109/elektro49696.2020.9130316 · Cited by 2

Compressing LSTM Networks with Hierarchical Coarse-Grain Sparsity

Kadetotad, D. V., Meng, J., Berisha, V., Chakrabarti, C., & Seo, J.-S. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1270 · Cited by 2

Human-Robot Interaction

Belpaeme, T. (2022) · The MIT Press eBooks · doi:10.7551/mitpress/13780.003.0025 · Cited by 2

Razpoznavanje slovenskega govora z metodami globokih nevronskih mrež

Ulčar, M., Dobrišek, S., & Robnik‐Šikonja, M. (2019) · Uporabna informatika · doi:10.31449/upinf.vol27.num3.53 · Cited by 3

Automatic Speech Recognition Post-Processing for Readability: Task, Dataset and a Two-Stage Pre-Trained Approach

Liao, J., Shi, Y., & Xu, Y. (2022) · IEEE Access · doi:10.1109/access.2022.3219838 · Cited by 5

Automatic Speaker Verification System Substantiating Children’s Dialects in School Settings

Kadyan, V., Bawa, P., Choudhary, R., & Dua, B. (2022) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc56933.2022.10053112 · Cited by 2

Voice Assistants for Therapeutic Support – A Literature Review

Siegert, I., Busch, M., Metzner, S., & Krüger, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35921-7_15 · Cited by 2

Alternative Access Technologies

Nisbet, P. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812487-1.00005-3 · Cited by 2

Improved Guided Source Separation Integrated with a Strong Back-End for the CHiME-6 Dinner Party Scenario

Chen, H., Zhang, P., Shi, Q., & Liu, Z. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1606 · Cited by 2

Deep Learning for Automated Medical Image AnalysisPreprint

Zhu, W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.04711 · Cited by 2

SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognitionPreprint

Huang, Z., Ng, T., Liu, L., Mason, H., Zhuang, X., & Liu, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.01992 · Cited by 2

On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural DynamicsPreprint

Feng, N., Zhang, G., & Khandelwal, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02784 · Cited by 3

A New Approach for Speech Keyword Spotting in Noisy Environment

Ye, P., & Duan, H. (2022) · International Conference on Artificial Intelligence, Soft Computing and Applications (AIAPP) · doi:10.5121/csit.2022.120206 · Cited by 1

XNOR-Nets with SETs: Proposal for a binarised convolution processing elements with Single-Electron Transistors

Bheemireddy, V. (2022) · Scientific Reports · doi:10.1038/s41598-022-13180-7 · Cited by 1

EGL: Efficient Graph Learning with Safety Constrains for Heterogeneous Trajectory Prediction

Lian, W., Wang, Y., Zeng, X., Liu, S., Xia, Y., Tang, H., & Su, H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35415-1_5 · Cited by 1

DigiHuman: A Conversational Digital Human with Facial Expressions

Özacar, K., & Özacar, K. (2024) · Turkish Journal of Science and Technology · doi:10.55525/tjst.1301324 · Cited by 1

eCommerce Search and Discovery

Kallumadi, S. T., & King, T. H. (2024) · ACM eBooks · doi:10.1145/3674127.3674140 · Cited by 1

STGT-Gen: Spatio-Temporal Graph Transformer for Multi-Vehicle Traffic Scenario Generation

Qin, X., Lu, C., Wei, Y., Fan, S., Song, Z., & Gong, J. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-01-7316 · Cited by 1

Advancing automatic speech recognition using feature fusion with self-supervised learning features: A case study on Fearless Steps Apollo corpus

Chen, S.-J., & Hansen, J. H. L. (2026) · Speech Communication · doi:10.1016/j.specom.2026.103380 · Cited by 1

Deaf and Hard of Hearing Access to Intelligent Personal Assistants: Comparison of Voice-Based Options with an LLM-Powered Touch Interface

DeVries, P. S., Okosi, M., Li, M., Dunphy, N., Gezae, G. W., Conway, D., Glasser, A., Kushalnagar, R., & Vogler, C. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791869 · Cited by 2

FIXED-POINT ANALYSIS OF MOBILENETS ON KEYWORD SPOTTING

AVRAM, A.-M., Coca, M., & Arseni, Ş.-C. (2019) · International Conference on Informatics in Economy · doi:10.12948/ie2019.01.02

Learning Contextual Language Embeddings for Monaural Multi-Talker Speech Recognition

Zhang, W., & Qian, Y. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2015 · Cited by 1

Stock Price Prediction Based on an Energy-Efficient Spiking-LSTM Hardware Accelerator

Wu, Y., Liu, Y., & Chen, T. P. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1828/1/012050 · Cited by 1

Weakly-Supervised Speech-to-Text Mapping with Visually Connected Non-Parallel Speech-Text Data Using Cyclic Partially-Aligned Transformer

Effendi, J., Sakti, S., & Nakamura, S. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-970 · Cited by 1

Always-On Speech Recognition Terminals: Designs based on approximate computing methods

Qin, L., Liu, Z., Yang, X., & Qiao, F. (2021) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2021.3126096 · Cited by 1

CycleGAN-Based Speech Mode Transformation Model for Robust Multilingual ASR

Tripathi, K., & Rao, K. (2022) · Circuits Systems and Signal Processing · doi:10.1007/s00034-022-02008-4 · Cited by 1

Capturing Temporal Information with LSTM to Stabilize A Rotating Machine

Wei, Y., Weiming, Q., Zhang, T., Deng, Y., Hu, F., Xiangqi, Z., Liu, T., Bolchover, P., Yu, T., Zheng, G., et al. (2022) · 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) · doi:10.1109/cvidliccea56201.2022.9825467 · Cited by 1

A Probabilistically-Oriented Analysis of the Performance of ASR Systems for Brazilian Radios and TVs

de Azevedo, D. M., Rodrigues, G. S., & Ladeira, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21689-3_13 · Cited by 1

An Effort Towards Improving Automatic-Transcription Systems

Biswas, A., Sabela, L. S., Sahu, P. K., & Samanta, R. K. (2022) · International Interdisciplinary Conference on Mathematics, Engineering and Science (MESIICON) · doi:10.1109/mesiicon55227.2022.10093297 · Cited by 1

Pedagogical Technologies for Remote Learning in Counselor Education: HyFlex Course Design

Mertes, A. P. (2023) · Rehabilitation counselors & educators journal · doi:10.52017/001c.84544 · Cited by 1

تأثير الذكاء الاصطناعي على الأسواق الدولية ومنشآت الأعمال

الهادي, م. (2024) · Compunet ( The Egyptian Information Journal ) · doi:10.21608/jstc.2024.361535 · Cited by 1

Pick and Place Control of a 3-DOF Robot Manipulator Based on Image and Pattern Recognition

Kariuki, S., Wanjau, E., Muchiri, I., Muguro, J. K., Njeri, W., & Sasaki, M. (2024) · Machines · doi:10.3390/machines12090665 · Cited by 1

Self-supervised learning using unlabeled speech with multiple types of speech disorder for disordered speech recognition

Takashima, R., Otani, T., Aihara, R., Takiguchi, T., & Taguchi, S. (2024) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3663548.3688536 · Cited by 1

Cross-language Retrieval

Galuščáková, P., Oard, D. W., & Nair, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674136 · Cited by 2

Audio-based Music Retrieval

Gómez, E., Cuesta, H., Gkiokas, A., Gómez-Cañón, J. S., Porcaro, L., & Yesiler, F. (2024) · ACM eBooks · doi:10.1145/3674127.3702959 · Cited by 1

Bias in Retrieval Systems

Baeza-Yates, R. A., Murgai, L., & Dudy, S. (2024) · ACM eBooks · doi:10.1145/3674127.3674138 · Cited by 1

Knowledge transfer for Russian conversational telephone automatic speech recognition

Romanenko, A., Matveev, Y. N., & Minker, W. (2018) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2018-18-2-236-242 · Cited by 1

BI-DIRECTIONAL LATTICE RECURRENT NEURAL NETWORKS FOR CONFIDENCE ESTIMATION

Li, Q., Ness, P. M., Ragni, A., & Gales, M. (2018) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.17863/cam.36745 · Cited by 1

Assessing Architectural Similarity in Populations of Deep Neural Networks

Chung, A. G., Fieguth, P., & Wong, A. K. C. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00070 · Cited by 1

Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification

Milacski, Z. Á., Póczos, B., & Lörincz, A. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8852057 · Cited by 1

Non-native speech recognition using audio style transfer

Radzikowski, K., Forc, M., Wang, L., Yoshie, O., & Nowak, R. M. (2019) · Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA) · doi:10.1117/12.2536535 · Cited by 1

High Performance Personal Adaptation Speech Recognition Framework by Incremental Learning with Plural Language Models

Ikegami, Y., Knauf, R., Damiani, E., Tsuruta, S., Sakurai, Y., Sakurai, E., Kutics, A., & Nakagawa, A. (2019) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2019.00081 · Cited by 1

Machine learning based forecasting of significant daily returns in foreign exchange markets

Kamalov, F., & Gurrib, I. (2021) · International Journal of Business Intelligence and Data Mining · doi:10.1504/ijbidm.2022.10043208 · Cited by 1

Joint Distance and Representation Learning for Sign Language Videos

Köse, Ö. D., & Saraçlar, M. (2020) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu49456.2020.9302207 · Cited by 1

Was kann Künstliche Intelligenz?

Paaß, G., & Hecker, D. (2020) · Künstliche Intelligenz · doi:10.1007/978-3-658-30211-5_2 · Cited by 1

Construction and Application of an Intelligent Response System for COVID-19 Voice Consultation in China: A Retrospective Study

Shi, J., Gao, J., Zhai, Y., Ye, M., Lu, Y., He, X., Cui, F., Ma, Q., & Zhao, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.781781 · Cited by 3

Deep Learning Architecture Reduction for fMRI Data

Alvarez-Gonzalez, R., & Méndez-Vázquez, A. (2022) · Brain Sciences · doi:10.3390/brainsci12020235 · Cited by 1

A Two-Stream Model Combining ResNet and Bi-LSTM Networks for Non-contact Dynamic Electrocardiogram Signal Quality Assessment

Zhu, G., Li, Y., Wu, Y., Lie, Z., Chen, C., & Chen, W. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-06368-8_21 · Cited by 1

Convolutional Neural Network and its Applications in Artificial Intelligence

Shawky, N. E. M. (2021) · Journal of ACS Advances in Computer Science · doi:10.21608/asc.2021.240051 · Cited by 1

Astral: An Autoencoder-Based Model for Pedestrian Trajectory Prediction of Variable-Length

Diao, Y., Su, Y., Zeng, X., Xu, C., Liu, S., & Su, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-11217-1_16 · Cited by 1

For Your Voice Only: Exploiting Side Channels in Voice Messaging for Environment Detection

Cardaioli, M., Conti, M., & Ravindranath, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17143-7_29 · Cited by 1

DASH: Scheduling Deep Learning Workloads on Multi-Generational GPU-Accelerated Clusters

Li, B., Patel, T., Gadepally, V. N., Gettings, K., Samsi, S., & Tiwari, D. (2022) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec55821.2022.9926390 · Cited by 2

Comparison of Automatic Speech Recognition Systems

Kim, J. Y., Liu, C., Calvo, R. A., McCabe, K., Taylor, S., Schuller, B. W., & Wu, K. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-5538-9_8 · Cited by 1

The Marchex 2018 English Conversational Telephone Speech Recognition SystemPreprint

Hahm, S., Orife, I., Walker, S., & Flaks, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.02058 · Cited by 1

LSTM neural network implementation using memristive crossbar circuits and its various topologies

Adam, K. (2018) · Nazarbayev University Repository (Nazarbayev University) · Cited by 1

An Inter-Layer Weight Prediction and Quantization for Deep Neural Networks based on a Smoothly Varying Weight HypothesisPreprint

Lee, K.-H., Jeong, J., & Bae, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.06835 · Cited by 1

Deep segmental phonetic posterior-grams based discovery of non-categories in L2 English speechPreprint

Li, X., Wu, X., Liu, X., & Meng, H. M. L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00205 · Cited by 1

Object recognition and tracking for surveillance applications using deep learning techniques

Dimou, A. (2020) · doi:10.20868/upm.thesis.65637 · Cited by 1

Learning Neural Models for Natural Language Processing in the Face of Distributional ShiftPreprint

Michel, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01558 · Cited by 2

Input Length Matters: An Empirical Study Of RNN-T And MWER Training For Long-form Telephony Speech Recognition.Preprint

Lu, Z., Pan, Y., Doutre, T., Cao, L., Prabhavalkar, R., Zhang, C., & Strohman, T. (2021) · arXiv (Cornell University) · Cited by 1

The Microsoft 2017 Conversational Speech Recognition System [Technical Report]

Xiong, W., Wu, L., Alleva, F. A., Droppo, J., Huang, X., & Stolcke, A. (2017) · Cited by 1

The microsoft 2016 conversational speech recognition system

Xiong, W., Droppo, J., Huang, X., Seide, F., Seltzer, M., Stolcke, A., Yu, D., & Zweig, G. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7953159 · Cited by 326

The CAPIO 2017 Conversational Speech Recognition SystemPreprint

Han, K. J., Chandrashekaran, A., Kim, J., & Lane, I. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.00059 · Cited by 73

Reducing word error rate on conversational speech from the Switchboard corpus

Jeanrenaud, P., Eide, E. M., Chaudhari, U. V., McDonough, J., Ng, K., Siu, M.-H., & Gish, H. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1995.479271 · Cited by 20

The IBM 2015 English conversational telephone speech recognition system

Saon, G., Kuo, H.-K. J., Rennie, S. J., & Picheny, M. (2015) · Interspeech (USB) · doi:10.21437/interspeech.2015-632 · Cited by 92