Cited by 72Speech Recognition and SynthesisVoice and Speech DisordersArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 207 of the 220 references it cites were found in a registry or an index.

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Open access

Zhang, Jun, Wu, Jingyue, Qiu, Yiyi, Song, Aiguo, Li, Weifeng, Li, Xin, Liu, Yecheng·2023·Computers in Biology and Medicine

DOI 10.1016/j.compbiomed.2022.106517PMC PMC9814440

Open this paper in CoChat — free account

Abstract

The growing and aging of the world population have driven the shortage of medical resources in recent years, especially during the COVID-19 pandemic. Fortunately, the rapid development of robotics and artificial intelligence technologies help to adapt to the challenges in the healthcare field. Among them, intelligent speech technology (IST) has served doctors and patients to improve the efficiency of medical behavior and alleviate the medical burden. However, problems like noise interference in complex medical scenarios and pronunciation differences between patients and healthy people hamper the broad application of IST in hospitals. In recent years, technologies such as machine learning have developed rapidly in intelligent speech recognition, which is expected to solve these problems. This paper first introduces IST's procedure and system architecture and analyzes its application in medical scenarios. Secondly, we review existing IST applications in smart hospitals in detail, including electronic medical documentation, disease diagnosis and evaluation, and human-medical equipment interaction. In addition, we elaborate on an application case of IST in the early recognition, diagnosis, rehabilitation training, evaluation, and daily care of stroke patients. Finally, we discuss IST's limitations, challenges, and future directions in the medical field. Furthermore, we propose a novel medical voice analysis system architecture that employs active hardware, active software, and human-computer interaction to realize intelligent and evolvable speech recognition. This comprehensive review and the proposed architecture offer directions for future studies on IST and its applications in smart hospitals.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1016/j.compbiomed.2022.106517

Cites

207 of 207 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Long Short-Term Memory
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
Davis, S., & Mermelstein, P. (1980) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1980.1163420 · Cited by 5,465
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
An Introduction to Convolutional Neural NetworksPreprint
O’Shea, K., & Nash, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.08458 · Cited by 4,082
An introduction to kernel-based learning algorithms
Müller, K. R., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001) · IEEE Transactions on Neural Networks · doi:10.1109/72.914517 · Cited by 3,521
Perceptual linear predictive (PLP) analysis of speech
Heřmanský, H. (1990) · The Journal of the Acoustical Society of America · doi:10.1121/1.399423 · Cited by 2,550
Principles of Voice Production
Titze, I. R., & Martin, D. W. (1998) · The Journal of the Acoustical Society of America · doi:10.1121/1.424266 · Cited by 1,643
New types of deep neural network learning for speech recognition and related applications: an overview
Deng, L., Hinton, G. E., & Kingsbury, B. E. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639344 · Cited by 1,259
Speaker identification and verification using Gaussian mixture speaker models
Reynolds, D. A. (1995) · Speech Communication · doi:10.1016/0167-6393(95)00009-d · Cited by 1,216
Searching and mining trillions of time series subsequences under dynamic time warping
Rakthanmanon, T., Campana, B. J. L., Mueen, A., Batista, G., Westover, M. B., Zhu, Q., Zakaria, J., & Keogh, E. (2012) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2339530.2339576 · Cited by 1,069
State-of-the-Art Speech Recognition with Sequence-to-Sequence Models
Chiu, C.-C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, E., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462105 · Cited by 1,043
Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech TestsPreprint
Tsanas, A., Little, M. A., McSharry, P., & Ramig, L. O. (2009) · Nature Precedings · doi:10.1109/tbme.2009.2036000 · Cited by 833
A survey on long short-term memory networks for time series prediction
Lindemann, B., Müller, T., Vietz, H., Jazdi, N., & Weyrich, M. (2021) · Procedia CIRP · doi:10.1016/j.procir.2021.03.088 · Cited by 831
Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings
Şakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbaş, A., Gürgen, F. S., Delil, Ş., Apaydın, H., & Kurşun, O. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2245674 · Cited by 703
Speech emotion recognition with deep convolutional neural networks
Issa, D., Demirci, M. F., & Yazıcı, A. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101894 · Cited by 621
The Natural History of Alzheimer's Disease: Description of Study Cohort and Accuracy of Diagnosis
Becker, J. T. (1994) · Archives of Neurology · doi:10.1001/archneur.1994.00540180063015 · Cited by 576
A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform
Sakar, C. O., Serbes, G., Gündüz, A., Tunç, H. C., Ozogur, H. N., Şakar, B. E., Tütüncü, M., Aydın, T., Isenkul, M. E., & Apaydın, H. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.10.022 · Cited by 555
The TORGO database of acoustic and articulatory speech from speakers with dysarthria
Rudzicz, F., Namasivayam, A. K., & Wolff, T. (2012) · Language Resources and Evaluation · doi:10.1007/s10579-011-9145-0 · Cited by 425
Dysarthric speech database for universal access research
Kim, H., Hasegawa-Johnson, M., Perlman, A., Gunderson, J., Huang, T. S., Watkin, K., & Frame, S. (2008) · Interspeech 2008 · doi:10.21437/interspeech.2008-480 · Cited by 397
The COUGHVID crowdsourcing dataset, a corpus for the study of large-scale cough analysis algorithms
Orlandic, L., Teijeiro, T., & Atienza, D. (2021) · Scientific Data · doi:10.1038/s41597-021-00937-4 · Cited by 346
openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor
Eyben, F. ;. (2013) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 322
COVID-19 cough classification using machine learning and global smartphone recordings
Pahar, M., Klopper, M., Warren, R., & Niesler, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104572 · Cited by 290
New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease
Orozco‐Arroyave, J. R., Arias-Londoño, J. D., Vargas‐Bonilla, J. F., Rátiva, M. C. G., & Nöth, E. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3c643i6hcfzo · Cited by 248
Smart Health Solution Integrating IoT and Cloud: A Case Study of Voice Pathology Monitoring
Muhammad, G., Rahman, S. M. M., Alelaiwi, A., & Alamri, A. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600425cm · Cited by 244
Assessment of physician’s knowledge, perception and willingness of telemedicine in Riyadh region, Saudi Arabia
Albarrak, A. I., Mohammed, R., Almarshoud, N., Almujalli, L., Aljaeed, R., Altuwaijiri, S., & Albohairy, T. (2019) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2019.04.006 · Cited by 209
Speech Technology for Healthcare: Opportunities, Challenges, and State of the Art
Latif, S., Qadir, J., Qayyum, A., Usama, M., & Younis, M. S. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3006860 · Cited by 189
Voice Pathology Detection Using Deep Learning on Mobile Healthcare Framework
Alhussein, M. A., & Muhammad, G. (2018) · IEEE Access · doi:10.1109/access.2018.2856238 · Cited by 188
Addressing voice recording replications for Parkinson’s disease detection
Naranjo, L., Pérez, C. J., Campos‐Roca, Y., & Martín, J. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.10.034 · Cited by 162
Convolutional neural network ensemble for Parkinson's disease detection from voice recordings
Hireš, M., Gazda, M., Drotár, P., Pah, N. D., Motin, M. A., & Kumar, D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105021 · Cited by 162
Assessment of Speech Intelligibility in Parkinson’s Disease Using a Speech-To-Text System
Dimauro, G., Nicola, V. D., Bevilacqua, V., Caivano, D., & Girardi, F. (2017) · IEEE Access · doi:10.1109/access.2017.2762475 · Cited by 149
A systematic review of speech recognition technology in health care
Johnson, M., Lapkin, S., Long, V., Sanchez, P., Suominen, H., Basilakis, J., & Dawson, L. (2014) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-14-94 · Cited by 141
Detection of Parkinson’s disease based on voice patterns ranking and optimized support vector machine
Lahmiri, S., & Shmuel, A. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.08.029 · Cited by 137
Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method
Chowdhury, N. K., Kabir, M. A., Rahman, M. M., & Islam, S. M. S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105405 · Cited by 131
Detection of tuberculosis by automatic cough sound analysis
Botha, G. H. R., Theron, G., Warren, R. M., Klopper, M., Dheda, K. U. J., van Helden, P. D., & Niesler, T. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aab6d0 · Cited by 130
Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice
Tuncer, T., Doğan, Ş., Özyurt, F., Belhouari, S. B., & Bensmail, H. (2020) · IEEE Access · doi:10.1109/access.2020.2992641 · Cited by 128
Wavelet time-frequency analysis and least squares support vector machines for the identification of voice disorders
Fonseca, E. S., Guido, R. C., Scalassara, P. R., Maciel, C. D., & Pereira, J. C. (2006) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2006.08.008 · Cited by 122
Some Commonly Used Speech Feature Extraction Algorithms
Alim, S. A., & Rashid, N. K. A. M. (2018) · IntechOpen eBooks · doi:10.5772/intechopen.80419 · Cited by 121
Automatic Speech Recognition from Neural Signals: A Focused Review
Herff, C., & Schultz, T. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00429 · Cited by 117
Electronic Health Record Interactions through Voice: A Review
Kumah-Crystal, Y. A., Pirtle, C. J., Whyte, H., Goode, E., Anders, S. H., & Lehmann, C. U. (2018) · Applied Clinical Informatics · doi:10.1055/s-0038-1666844 · Cited by 113
Detecting Parkinson’s disease from sustained phonation and speech signals
Vaičiukynas, E., Verikas, A., Gelžinis, A., & Bačauskienė, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0185613 · Cited by 110
Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping
Ismail, A., Abdelrazek, S. M., & Elhenawy, I. (2020) · Sustainability · doi:10.3390/su12062403 · Cited by 110
COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features
Pahar, M., Klopper, M., Warren, R. M., & Niesler, T. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105153 · Cited by 109
PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates
Bhosale, Y. H., & Patnaik, K. S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104445 · Cited by 109
Classification of silent speech using support vector machine and relevance vector machine
Matsumoto, M., & Hori, J. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2013.10.023 · Cited by 108
An intelligent healthcare system for detection and classification to discriminate vocal fold disorders
Ali, Z., Hossain, M. S., Muhammad, G., & Sangaiah, A. K. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.02.021 · Cited by 103
Investigation of Eye-Tracking Scan Path as a Biomarker for Autism Screening Using Machine Learning Algorithms
Kanhirakadavath, M. R., & Chandran, M. S. M. (2022) · Diagnostics · doi:10.3390/diagnostics12020518 · Cited by 103
Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review
Bhosale, Y. H., & Patnaik, K. S. (2022) · Neural Processing Letters · doi:10.1007/s11063-022-11023-0 · Cited by 101
Speech recognition for clinical documentation from 1990 to 2018: a systematic review
Blackley, S. V., Huynh, J., Wang, L., Korach, Z., & Zhou, L. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy179 · Cited by 97
A new database of healthy and pathological voices
Cesari, U., Pietro, G. D., Marciano, E., Niri, C., Sannino, G., & Verde, L. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.04.008 · Cited by 92
An Automatic Health Monitoring System for Patients Suffering From Voice Complications in Smart Cities
Ali, Z., Muhammad, G., & Alhamid, M. F. (2017) · IEEE Access · doi:10.1109/access.2017.2680467 · Cited by 90
Enhanced estimations of post‐stroke aphasia severity using stacked multimodal predictions
Pustina, D., Coslett, H. B., Ungar, L., Faseyitan, O. K., Medaglia, J. D., Avants, B., & Schwartz, M. F. (2017) · Human Brain Mapping · doi:10.1002/hbm.23752 · Cited by 86
A New Hybrid Intelligent Framework for Predicting Parkinson’s Disease
Cai, Z., Gu, J., & Chen, H. (2017) · IEEE Access · doi:10.1109/access.2017.2741521 · Cited by 85
Respiratory Sound Based Classification of Chronic Obstructive Pulmonary Disease: a Risk Stratification Approach in Machine Learning Paradigm
Haider, N. S., Singh, B. K., Periyasamy, R., & Behera, A. K. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1388-0 · Cited by 84
Automatic intelligibility classification of sentence-level pathological speech
Kim, J., Kumar, N., Tsiartas, A., Li, M., & Narayanan, S. S. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2014.02.001 · Cited by 83
Automatic cough classification for tuberculosis screening in a real-world environment
Pahar, M., Klopper, M., Reeve, B. W. P., Warren, R. M., Theron, G., & Niesler, T. (2021) · Physiological Measurement · doi:10.1088/1361-6579/ac2fb8 · Cited by 83
Parkinson’s Disease Detection Based on Running Speech Data From Phone Calls
Laganas, C., Iakovakis, D., Hadjidimitriou, S., Charisis, V., Dias, S. B., Bostantzopoulou, S., Katsarou, Z., Klingelhoefer, L., Reichmann, H., Trivedi, D., et al. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3116935 · Cited by 83
Dementia detection using automatic analysis of conversations
Mirheidari, B., Blackburn, D., Walker, T., Reuber, M., & Christensen, H. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2018.07.006 · Cited by 82
Towards robust voice pathology detection
Harár, P., Galáž, Z., Alonso, J. B., Mekyska, J., Burget, R., & Smékal, Z. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3464-7 · Cited by 80
A clinician survey of using speech recognition for clinical documentation in the electronic health record
Goss, F. R., Blackley, S. V., Ortega, C. A., Kowalski, L. T., Landman, A., Lin, C., Meteer, M. W., Bakes, S., Gradwohl, S. C., Bates, D. W., et al. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.07.017 · Cited by 77
Exploring the Use of Artificial Intelligence Techniques to Detect the Presence of Coronavirus Covid-19 Through Speech and Voice Analysis
Verde, L., Pietro, G. D., Ghoneim, A., Alrashoud, M. R., Al-Mutib, K. N., & Sannino, G. (2021) · IEEE Access · doi:10.1109/access.2021.3075571 · Cited by 76
Recurrent neural network with backpropagation through time for speech recognition
Ahmad, A., Ismail, S., & Samaon, D. F. (2005) · International Symposium on Communications and Information Technologies · doi:10.1109/iscit.2004.1412458 · Cited by 76
Saarbruecken Voice Database
Woldert-Jokisz, B. (2007) · Cited by 74
Cloud based framework for Parkinson’s disease diagnosis and monitoring system for remote healthcare applications
Mamun, K. A., Alhussein, M. A., Sailunaz, K., & Islam, M. S. (2015) · Future Generation Computer Systems · doi:10.1016/j.future.2015.11.010 · Cited by 74
Remote Speech Analysis in the Evaluation of Hospitalized Patients With Acute Decompensated Heart Failure
Amir, O., Abraham, W. T., Azzam, Z. S., Berger, G., Anker, S. D., Pinney, S., Burkhoff, D., Shallom, I. D., Lotan, C., & Edelman, E. R. (2021) · JACC Heart Failure · doi:10.1016/j.jchf.2021.08.008 · Cited by 73
Physician use of speech recognition versus typing in clinical documentation: A controlled observational study
Blackley, S. V., Schubert, V. D., Goss, F. R., Assad, W. A., Garabedian, P. M., & Zhou, L. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104178 · Cited by 70
Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection
Muhammad, G., & Alhussein, M. (2021) · IEEE Access · doi:10.1109/access.2021.3090317 · Cited by 70
Automated spoken dialogue system for hypertensive patient home management
Giorgino, T., Azzini, I., Rognoni, C., Quaglini, S., Stefanelli, M., Gretter, R., & Falavigna, D. (2005) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2004.04.026 · Cited by 69
Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system
Muhammad, G. (2015) · Cluster Computing · doi:10.1007/s10586-015-0439-7 · Cited by 69
Machine Learning Methods with Decision Forests for Parkinson’s Detection
Pramanik, M., Pradhan, R., Nandy, P., Bhoi, A. K., & Barsocchi, P. (2021) · Applied Sciences · doi:10.3390/app11020581 · Cited by 64
Efficiency and safety of speech recognition for documentation in the electronic health record
Hodgson, T., Magrabi, F., & Coiera, E. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx073 · Cited by 63
Epidemiology of Voice Disorders in Latvian School Teachers
Trinīte, B. (2016) · Journal of Voice · doi:10.1016/j.jvoice.2016.10.014 · Cited by 60
Deep neural network for automatic classification of pathological voice signals
Chen, L., & Chen, J. (2022) · Journal of Voice · doi:10.1016/j.jvoice.2020.05.029 · Cited by 60
Surface roughness diagnosis in hard turning using acoustic signals and support vector machine: A PCA-based approach
Papandréa, P. J., Frigieri, E. P., Maia, P. R., de Oliveira, L. G., & de Paiva, A. P. (2019) · Applied Acoustics · doi:10.1016/j.apacoust.2019.107102 · Cited by 59
Feature selection for spontaneous speech analysis to aid in Alzheimer's disease diagnosis: A fractal dimension approach
de Ipiña, K. L., Solé‐Casals, J., Eguiraun, H., Alonso, J. B., Travieso, C. M., Ezeiza, A., Barroso, N., Ecay‐Torres, M., Martínez‐Lage, P., & Beitia, B. (2014) · Computer Speech & Language · doi:10.1016/j.csl.2014.08.002 · Cited by 58
Automatic Assessment of Sentence-Level Dysarthria Intelligibility Using BLSTM
Bhat, C. G., & Strik, H. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2967652 · Cited by 58
IoT Deployable Lightweight Deep Learning Application For COVID-19 Detection With Lung Diseases Using RaspberryPi
Bhosale, Y. H., & Patnaik, K. S. (2022) · International Conference on IoT and Blockchain Technology (ICIBT) · doi:10.1109/icibt52874.2022.9807725 · Cited by 58
Extended Reality (XR) and telehealth interventions for children or adolescents with autism spectrum disorder: Systematic review of qualitative and quantitative studies
Chen, Y., Zhou, Z., Cao, M., Liu, M., Lin, Z., Yang, W., Yang, X., Dhaidhai, D. T., & Xiong, P. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104683 · Cited by 57
A field study of the impact of gender and user's technical experience on the performance of voice-activated medical tracking application
Rodger, J. A., & Pendharkar, P. C. (2003) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2003.09.005 · Cited by 56
Fusion of WPT and MFCC feature extraction in Parkinson’s disease diagnosis
Kuresan, H., Dhanalakshmi, S., & Masunda, S. (2019) · Technology and Health Care · doi:10.3233/thc-181306 · Cited by 55
Automatic Grading of Palsy Using Asymmetrical Facial Features: A Study Complemented by New Solutions
Sajid, M., Shafique, T., Baig, M. J. A., Riaz, I., Amin, S., & Manzoor, S. (2018) · Symmetry · doi:10.3390/sym10070242 · Cited by 54
Automatic Voice Pathology Monitoring Using Parallel Deep Models for Smart Healthcare
Alhussein, M. A., & Muhammad, G. (2019) · IEEE Access · doi:10.1109/access.2019.2905597 · Cited by 51
Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires
Shimon, C., Shafat, G., Dangoor, I., & Ben-Shitrit, A. (2021) · The Journal of the Acoustical Society of America · doi:10.1121/10.0003434 · Cited by 51
An attention Long Short-Term Memory based system for automatic classification of speech intelligibility
Fernández-Díaz, M., & Gallardo-Antolín, A. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103976 · Cited by 50
Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough
Ponomarchuk, A., Burenko, I., Malkin, E., Nazarov, I., Kokh, V., Avetisian, M., & Евгеньевич, Ж. Л. (2022) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2022.3142514 · Cited by 50
Automatic Intelligibility Assessment of Dysarthric Speech Using Phonologically-Structured Sparse Linear Model
Kim, M. J., Kim, Y., & Kim, H. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2403619 · Cited by 49
Vocal Folds Disorder Detection using Pattern Recognition Methods
Wang, J., & Jo, C. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353023 · Cited by 49
A Hybrid Speech Enhancement Algorithm for Voice Assistance Application
Gnanamanickam, J., Natarajan, Y., & Preethaa, K. R. S. (2021) · Sensors · doi:10.3390/s21217025 · Cited by 48
Dysarthric-speech detection using transfer learning with convolutional neural networks
Sekhar, S. R. M., Kashyap, G., Bhansali, A., Azhagesan, A. A. A., & Singh, K. (2021) · ICT Express · doi:10.1016/j.icte.2021.07.004 · Cited by 47
An intelligent listening framework for capturing encounter notes from a doctor-patient dialog
Klann, J. G., & Peter Szolovits, B. (2009) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-9-s1-s3 · Cited by 46
Automated screening for distress: A perspective for the future
Rana, R. K., Latif, S., Gururajan, R., Gray, A. D., Mackenzie, G., Humphris, G. M., & Dunn, J. C. (2019) · European Journal of Cancer Care · doi:10.1111/ecc.13033 · Cited by 46
Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques
Huang, Y.-J., Lin, Y.-T., Liu, C.-C., Lee, L.-E., Hung, S.-H., Lo, J.-K., & Fu, L.-C. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3163777 · Cited by 46
Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis
Stasak, B., Huang, Z., Razavi, S., Joachim, D., & Epps, J. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00090-4 · Cited by 45
A Mobile-based Virtual Reality Speech Rehabilitation App for Patients With Aphasia After Stroke: Development and Pilot Usability Study
Bu, X., Ng, P. H. F., Tong, Y., Chen, P. Q., Fan, R., Tang, Q., Cheng, Q., Li, S., Cheng, A. S. K., & Liu, X. (2022) · JMIR Serious Games · doi:10.2196/30196 · Cited by 45
Ensemble of deep neural networks using acoustic environment classification for statistical model-based voice activity detection
Hwang, I., Park, H., & Chang, J. (2015) · Computer Speech & Language · doi:10.1016/j.csl.2015.11.003 · Cited by 43
A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment
Lauraitis, A., Maskeliūnas, R., Damaševičius, R., & Krilavičius, T. (2020) · Sensors · doi:10.3390/s20113236 · Cited by 43
Leveraging Artificial Intelligence to Improve Voice Disorder Identification Through the Use of a Reliable Mobile App
Verde, L., Pietro, G. D., Alrashoud, M. R., Ghoneim, A., Al-Mutib, K. N., & Sannino, G. (2019) · IEEE Access · doi:10.1109/access.2019.2938265 · Cited by 42
Integrating articulatory data in deep neural network-based acoustic modeling
Badino, L., Canevari, C., Fadiga, L., & Giorgio Metta, B. (2015) · Computer Speech & Language · doi:10.1016/j.csl.2015.05.005 · Cited by 41
Enhanced Living by Assessing Voice Pathology Using a Co-Occurrence Matrix
Muhammad, G., Alhamid, M. F., Hossain, M. A., Almogren, A., & Vasilakos, A. V. (2017) · Sensors · doi:10.3390/s17020267 · Cited by 41
A wavelet- and neural network-based voice system for a smart wheelchair control
Al‐Rousan, M., & Assaleh, K. T. (2009) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2009.02.005 · Cited by 39
Automatic assessment of intelligibility in speakers with dysarthria from coded telephone speech using glottal features
Narendra, N. P., & Alku, P. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101117 · Cited by 39
Assessing clinical utility of machine learning and artificial intelligence approaches to analyze speech recordings in multiple sclerosis: A pilot study
Svoboda, E., Bořil, T., Rusz, J., Tykalová, T., Horáková, D., Guttmann, C. R. G., Blagoev, K. B., Hatabu, H., & Valtchinov, V. I. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105853 · Cited by 39
An optimum end-to-end text-independent speaker identification system using convolutional neural network
Farsiani, S., Izadkhah, H., & Lotfi, S. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107882 · Cited by 39
Improving the Utility of Speech Recognition Through Error Detection
Voll, K. D., Atkins, M. S., & Forster, B. B. (2007) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-007-9034-7 · Cited by 38
Deep assessment process: Objective assessment process for unilateral peripheral facial paralysis via deep convolutional neural network
Guo, Z., Shen, M., Duan, L., Zhou, Y., Xiang, J., Ding, H., Chen, S., Deußen, O., & Guo, D. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950486 · Cited by 38
Support vector wavelet adaptation for pathological voice assessment
Erfanian Saeedi, N., Almasganj, F., & Torabinejad, F. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.06.019 · Cited by 37
Automatic speech recognition (ASR) based approach for speech therapy of aphasic patients: A review
Jamal, N., Shanta, S., Mahmud, F., & Sha'abani, M. N. A.-H. (2017) · AIP conference proceedings · doi:10.1063/1.5002046 · Cited by 37
Assessment of speech impairment in patients with Parkinson's disease from acoustic quantifications of oral diadochokinetic sequences
Karlsson, F., Schalling, E., Laakso, K., Johansson, K. M., & Hartelius, L. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0000581 · Cited by 37
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
Yılmaz, E., Mitra, V., Sivaraman, G., & Franco, H. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.05.002 · Cited by 36
Speech Recognition using Artificial Neural Networks and Hidden Markov Models
Al‐Alaoui, M. A., Al‐Kanj, L., Azar, J. C., & Yaacoub, E. E. (2008) · Cited by 35
Optimal feature selection for the assessment of vocal fold disorders
Heris, H. K., Aghazadeh, B. S., & Nikkhah‐Bahrami, M. (2009) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2009.06.014 · Cited by 35
Development and Validation of a Robust Speech Interface for Improved Human-Robot Interaction
Atrash, A., Kaplow, R., Villemure, J., West, R., Yamani, H., & Pineau, J. (2009) · International Journal of Social Robotics · doi:10.1007/s12369-009-0032-4 · Cited by 35
Using Knee Acoustical Emissions for Sensing Joint Health in Patients With Juvenile Idiopathic Arthritis: A Pilot Study
Semiz, B., Hersek, S., Whittingslow, D. C., Ponder, L. A., Prahalad, S., & Inan, O. T. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2869990 · Cited by 35
Wearable Long-Term Social Sensing for Mental Wellbeing
Jiang, L., Gao, B., Gu, J. J., Chen, Y., Gao, Z., Ma, X., Kendrick, K. M., & Woo, W. L. (2018) · IEEE Sensors Journal · doi:10.1109/jsen.2018.2877427 · Cited by 35
Acoustical Assessment of Voice Disorder With Continuous Speech Using ASR Posterior Features
Liu, Y., Lee, T., Law, T. K. T., & Lee, K. Y.-S. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2905778 · Cited by 35
Self-citation
Augmenting the Impact of Technology Adoption With Financial Incentive to Improve Radiology Report Signature Times
Andriole, K. P., Prevedello, L. M. S., Dufault, A., Pezeshk, P., Bransfield, R., Hanson, R., Doubilet, P. M., Seltzer, S. E., & Khorasani, R. (2010) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2009.11.011 · Cited by 34
Incidence of speech recognition errors in the emergency department
Goss, F. R., Zhou, L., & Weiner, S. G. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.05.005 · Cited by 34
A mel-frequency cepstral coefficient-based approach for surface roughness diagnosis in hard turning using acoustic signals and gaussian mixture models
Frigieri, E. P., Campos, P. H. S., de Paiva, A. P., Balestrassi, P. P., Ferreira, J. R., & Ynoguti, C. A. (2016) · Applied Acoustics · doi:10.1016/j.apacoust.2016.06.027 · Cited by 34
Conditional GAN based augmentation for predictive modeling of respiratory signals
Jayalakshmy, S., & Sudha, G. F. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104930 · Cited by 34
Automatic Non-Invasive Cough Detection based on Accelerometer and Audio Signals
Pahar, M., Miranda, I. D. D. S., Diacon, A. H., & Niesler, T. (2022) · Journal of Signal Processing Systems · doi:10.1007/s11265-022-01748-5 · Cited by 33
Fused Mel Feature sets based Text-Independent Speaker Identification using Gaussian Mixture Model
Kumari, R. S. S., Nidhyananthan, S. S., & Anand., G. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.01.867 · Cited by 32
Subspace-Based Learning for Automatic Dysarthric Speech Detection
Janbakhshi, P., Kodrasi, I., & Bourlard, H. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3044503 · Cited by 32
Novel third-order hidden Markov models for speaker identification in shouted talking environments
Shahin, I. M. A. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.07.006 · Cited by 30
Speech Recognition for Medical Dictation: Overview in Quebec and Systematic Review
Poder, T. G., Fisette, J., & Déry, V. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0947-0 · Cited by 30
Deep learning-based smart speaker to confirm surgical sites for cataract surgeries: A pilot study
Yoo, T. K., Oh, E., Kim, H. K., Ryu, I. H., Lee, I. S., Kim, J. S., & Kim, J. K. (2020) · PLoS ONE · doi:10.1371/journal.pone.0231322 · Cited by 30
The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology
Kishimoto, T., Takamiya, A., Liang, K., Funaki, K., Fujita, T., Kitazawa, M., Yoshimura, M., Tazawa, Y., Horigome, T., Eguchi, Y., et al. (2020) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2020.100649 · Cited by 30
Deep Convolutional Neural Network Based Covid-19 Classification From Radiology X-Ray Images For IoT Enabled Devices
Bhosale, Y. H., Zanwar, S. R., Ahmed, Z., Nakrani, M., Bhuyar, D. L., & Shinde, U. B. (2022) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs54159.2022.9785113 · Cited by 30
Dysarthria intelligibility assessment in a factor analysis total variability space
Martínez, D., Green, P., & Christensen, H. (2013) · Interspeech (USB) · doi:10.21437/interspeech.2013-505 · Cited by 29
Fly Ormia Ochracea Inspired MEMS Directional Microphone: A Review
Ishfaque, A., & Kim, B. (2017) · IEEE Sensors Journal · doi:10.1109/jsen.2017.2787862 · Cited by 28
A Group Decision Optimization Analogy-Based Deep Learning Architecture for Multiclass Pathology Classification in a Voice Signal
Kumar, W. K., Singh, M. P., Nongmeikapam, K., & Singh, A. D. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3049277 · Cited by 28
Computer Audition for Fighting the SARS-CoV-2 Corona Crisis—Introducing the Multitask Speech Corpus for COVID-19
Qian, K., Schmitt, M., Zheng, H., Koike, T., Han, J., Liu, J., Ji, W., Duan, J., Song, M., Yang, Z., et al. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3067605 · Cited by 28
A Facial and Vocal Expression Based Comprehensive Framework for Real-Time Student Stress Monitoring in an IoT-Fog-Cloud Environment
Singh, M., Bharti, S., Kaur, H., Arora, V., Saini, M., Kaur, M., & Singh, J. (2022) · IEEE Access · doi:10.1109/access.2022.3183077 · Cited by 28
Part of Speech Tagging in Odia Using Support Vector Machine
Das, B. R., Sahoo, S., Panda, C. S., & Patnaik, S. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.04.127 · Cited by 27
Text-dependent speaker verification based on i-vectors, Neural Networks and Hidden Markov Models
Zeinali, H., Sameti, H., Burget, L., & Černocký, J. (2017) · Computer Speech & Language · doi:10.1016/j.csl.2017.04.005 · Cited by 27
Combining acoustic signals and medical records to improve pathological voice classification
Fang, S., Wang, C., Chen, J., Tsao, Y., & Lin, F. (2019) · APSIPA Transactions on Signal and Information Processing · doi:10.1017/atsip.2019.7 · Cited by 27
Automated speech recognition for time recording in out-of-hospital emergency medicine—an experimental approach
Gröschel, J., Philipp, F., Skonetzki, S., Genzwürker, H. V., Wetter, T., & Ellinger, K. (2004) · Resuscitation · doi:10.1016/j.resuscitation.2003.10.006 · Cited by 26
Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results
Jonell, P., Moëll, B., Håkansson, K., Henter, G. E., Kucherenko, T., Mikheeva, O., Hagman, G., Holleman, J., Kivipelto, M., Kjellström, H., et al. (2021) · Frontiers in Computer Science · doi:10.3389/fcomp.2021.642633 · Cited by 26
TripletCough: Cougher Identification and Verification From Contact-Free Smartphone-Based Audio Recordings Using Metric Learning
Jokic, S., Cleres, D., Rassouli, F., Steurer-Stey, C., Puhan, M. A., Brutsche, M. H., Fleisch, E., & Barata, F. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3152944 · Cited by 26
Autoregressive Hidden Markov Model and the Speech Signal
Bryan, J., & Levinson, S. E. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.09.151 · Cited by 25
NUVA: A Naming Utterance Verifier for Aphasia Treatment
Barbera, D. S., Huckvale, M. A., Fleming, V., Upton, E., Coley-Fisher, H., Doogan, C., Shaw, I., Latham, W., Leff, A. P., & Crinion, J. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101221 · Cited by 25
Automatic Prediction of Speech Intelligibility Based on X-Vectors in the Context of Head and Neck Cancer
Quintas, S., Mauclair, J., Woisard, V., & Pinquier, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1431 · Cited by 25
A usability framework for speech recognition technologies in clinical handover: A pre-implementation study
Dawson, L., Johnson, M., Suominen, H., Basilakis, J., Sanchez, P., Estival, D., Kelly, B. F., & Hanlen, L. (2014) · Journal of Medical Systems · doi:10.1007/s10916-014-0056-7 · Cited by 24
Automatic Detection and Classification of Systolic and Diastolic Profiles of PCG Corrupted Due to Limitations of Electronic Stethoscope Recording
Babu, K. A., & Ramkumar, B. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3028373 · Cited by 24
Wavelet adaptation for automatic voice disorders sorting
Saeedi, N. E., & Almasganj, F. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.03.006 · Cited by 23
On preprocessing of speech signals
Mitra, B. K. (2009) · Sussex Research Online (University of Sussex) · Cited by 23
Evaluation and comparison of errors on nursing notes created by online and offline speech recognition technology and handwritten: an interventional study
Peivandi, S., Ahmadian, L., Farokhzadian, J., & Jahani, Y. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01835-4 · Cited by 23
Automatic speech recognition in the operating room – An essential contemporary tool or a redundant gadget? A survey evaluation among physicians in form of a qualitative study
Schulte, A., Suarez-Ibarrola, R., Wegen, D., Pohlmann, P.-F., Petersen, E. L., & Miernik, A. J. (2020) · Annals of Medicine and Surgery · doi:10.1016/j.amsu.2020.09.015 · Cited by 21
Speaker identification in stressful talking environments based on convolutional neural network
Shahin, I. M. A., Nassif, A. B., & Hindawi, N. A. A. (2021) · International Journal of Speech Technology · doi:10.1007/s10772-021-09869-1 · Cited by 20
Speech Paralinguistic Approach for Detecting Dementia Using Gated Convolutional Neural Network
Mariana, R. M., Makiuchi, M. R., Tifani, W., Warnita, T., 中順, 井., Inoue, N., 浩一, 篠., Shinoda, K., Yoshimura, M., Kitazawa, M., et al. (2021) · Tokyo Tech Research Repository (Tokyo Institute of Technology) · Cited by 20
Speech enhancement using Maximum A-Posteriori and Gaussian Mixture Models for speech and noise Periodogram estimation
Chehrehsa, S., & Moir, T. J. (2015) · Computer Speech & Language · doi:10.1016/j.csl.2015.09.001 · Cited by 19
On combining acoustic and modulation spectrograms in an attention LSTM-based system for speech intelligibility level classification
Gallardo-Antolín, A., & Montero, J. M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.065 · Cited by 19
TripletCough: Cougher Identification and Verification From Contact-Free Smartphone-Based Audio Recordings Using Metric Learning
Jokic, S., Cleres, D., Rassouli, F., et al. (2022) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000537595 · Cited by 19
An intelligent framework for simulating robot-assisted surgical operations
Valencia‐García, R., Martínez‐Béjar, R., & Gasparetto, A. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2004.12.003 · Cited by 18
Depression detection based on linear and nonlinear speech features in I-vector/SVDA framework
Mobram, S., & Vali, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105926 · Cited by 17
Speech Recognition Using Scaly Neural Networks
Othman, A. M., & Riadh, M. H. (2008) · doi:10.5281/zenodo.1333111 · Cited by 16
An Evaluation of the Effect of Anxiety on Speech — Computational Prediction of Anxiety from Sustained Vowels
Baird, A., Cummins, N., Schnieder, S., Krajewski, J., & Schuller, B. W. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1801 · Cited by 16
Comparing Ensemble-Based Machine Learning Classifiers Developed for Distinguishing Hypokinetic Dysarthria from Presbyphonia
Byeon, H. (2021) · Applied Sciences · doi:10.3390/app11052235 · Cited by 16
Microscale Devices for Biomimetic Sound Source Localization: A Review
Rahaman, A., & Kim, B. (2021) · Journal of Microelectromechanical Systems · doi:10.1109/jmems.2021.3129282 · Cited by 16
COVID-19 Diagnosis from Crowdsourced Cough Sound Data
Son, M.-J., & Lee, S.-P. (2022) · Applied Sciences · doi:10.3390/app12041795 · Cited by 16
Digitale Spracherkennung bei der Erfassung computertomographischer Befundtexte
Teichgräber, U. K.-M., Ehrenstein, T., Lemke, M. N., Liebig, T. M., Stobbe, H., Hosten, N., Keske, U., & Felix, R. (1999) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/s-1999-11094 · Cited by 15
Automatic speech recognition — Can it improve the man-machine interface in medical expert systems?
Landau, J. A., Norwich, K. H., & Evans, S. (1989) · International Journal of Bio-Medical Computing · doi:10.1016/0020-7101(89)90013-5 · Cited by 15
PANACEA Cough Sound-Based Diagnosis of COVID-19 for the DiCOVA 2021 Challenge
Kamble, M. R., González, J. A., Grau, T., Espín, J. M., Cascioli, L., Huang, Y., Gomez-Alanis, A., Patino, J., Font, R., Peinado, A. M., et al. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1062 · Cited by 15
Accuracy of Cloud-Based Speech Recognition Open Application Programming Interface for Medical Terms of Korean
Lee, S. H., Park, J., Yang, K., Min, J., & Choi, J. (2022) · Journal of Korean Medical Science · doi:10.3346/jkms.2022.37.e144 · Cited by 15
Advanced computing solutions for analysis of laryngeal disorders
Türkmen, H. İ., & Karsligil, M. E. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-02031-9 · Cited by 14
Combination of multiple speech dimensions for automatic assessment of dysarthric speech intelligibility
Kim, M. J., & Kim, H. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-317 · Cited by 14
Coordinated control of an intelligentwheelchair based on a brain-computer interface and speech recognition
Wang, H., Li, Y., & Yu, T. (2014) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/jzus.c1400150 · Cited by 13
Improving hearing-aid gains based on automatic speech recognition
Fontan, L., Coz, M. L., Azzopardi, C., Stone, M. A., & Füllgrabe, C. (2020) · The Journal of the Acoustical Society of America · doi:10.1121/10.0001866 · Cited by 13
Cloud‐assisted Parkinson disease identification system for remote patient monitoring and diagnosis in the smart healthcare applications
Harimoorthy, K., & Menakadevi, T. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6419 · Cited by 13
Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms
Villa-Parra, A. C., Criollo, I., Valadão, C., Silva, L., Coelho, Y., Lampier, L., Rangel, L., Sharma, G., Delisle-Rodríguez, D., Calle-Sigüencia, J., et al. (2022) · Sensors · doi:10.3390/s22124341 · Cited by 13
Online Personalization of Compression in Hearing Aids via Maximum Likelihood Inverse Reinforcement Learning
Akbarzadeh, S., Lobariñas, E., & Kehtarnavaz, N. (2022) · IEEE Access · doi:10.1109/access.2022.3178594 · Cited by 13
Predicting Severity of Voice Disorder from DNN-HMM Acoustic Posteriors
Lee, T., Liu, Y., Yeung, Y. T., Law, T. K. T., & Lee, K. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1098 · Cited by 13
Self-citation
Multicondition Training for Noise-Robust Detection of Benign Vocal Fold Lesions From Recorded Speech
Madruga, M., Campos‐Roca, Y., & Pérez, C. J. (2020) · IEEE Access · doi:10.1109/access.2020.3046873 · Cited by 12
Sahayantra - A Patient Assistance Robot
Pranathi, B. S., Nair, A. S., Anushree, C. S., & Chandar, T. S. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225444 · Cited by 11
Voice Biomarkers of Recovery From Acute Respiratory Illness
Tracey, B., Patel, S., Zhang, Y., Chappie, K., Volfson, D., Parisi, F., Adans-Dester, C., Bertacchi, F., Bonato, P., & Wacnik, P. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3137050 · Cited by 10
Speaker verification using fundamental frequency
Cheng, Y., & Leung, H. C. (1998) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.1998-220 · Cited by 9
Combining Phone Posteriorgrams from Strong and Weak Recognizers for Automatic Speech Assessment of People with Aphasia
Qin, Y. H., Lee, T., & Kong, A. P. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683835 · Cited by 9
Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring
Pahar, M., Klopper, M., Reeve, B. W. P., Warren, R. M., Theron, G., Diacon, A. H., & Niesler, T. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909522 · Cited by 9
A Novel Arabic Text-independent Speaker Verification System based on Fuzzy Hidden Markov Model
Ghoniem, R. M., & Shaalan, K. F. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.10.119 · Cited by 8
Dialogue enabling speech-to-text user assistive agent with auditory perceptual beamforming for hearing-impaired
Lee, S., Kang, S.-M., Ko, H., Yoon, J., & Keum, M.-S. (2013) · International Conference on Consumer Electronics · doi:10.1109/icce.2013.6486929 · Cited by 8
Dialogue enabling speech-to-text user assistive agent system for hearing-impaired person
Lee, S., Kang, S.-M., Han, D. K., & Ko, H. (2016) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1447-8 · Cited by 7
Development of Smart Healthcare System for Visually Impaired using Speech Recognition
Almutairi, K., Ismail, A., Abdelrazek, S. M., & El-Bakry, H. M. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111275 · Cited by 7
A Systematic Review of Machine Learning based Automatic Speech Assessment System to Evaluate Speech Impairment
Jothi, K. R., & Mamatha, V. L. (2020) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iciss49785.2020.9315920 · Cited by 7
Detection of Parkinsons Disease Via a Multi-Modal Approach
Gandhi, J., Gadekar, A., Rajabally, T., Vinayakray-Jani, P., & Ambawade, D. D. (2021) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt51525.2021.9580135 · Cited by 7
Semantic and phonetic automatic reconstruction of medical dictations
Petrik, S., Drexel, C., Fessler, L., Jancsary, J., Klein, A., Kubin, G., Matiasek, J., Pernkopf, F., & Trost, H. (2010) · Computer Speech & Language · doi:10.1016/j.csl.2010.07.003 · Cited by 6
Speech Recognition as a Practice Tool for Dysarthria
Fager, S. K. (2017) · Seminars in Speech and Language · doi:10.1055/s-0037-1602841 · Cited by 6
Wearable Band-Shaped Device and Detection Algorithm for Laryngeal Elevation in Mendelsohn Maneuver
Nakamoto, H., Katsuno, Y., Yamamoto, A., Umehara, K., Bessho, Y., Kobayashi, F., & Ishikawa, A. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3034304 · Cited by 6
Design and implementation of a smart reader for blind and visually impaired people
Punith, A., Manish, G. S., Sumanth, M. S., Vinay, A., Karthik, R., & Jyothi, K. S. (2021) · AIP conference proceedings · doi:10.1063/5.0036140 · Cited by 6
Significant sensors and parameters in assessment of dysarthric speech
Dhanalakshmi, M., Nagarajan, T., & Vijayalakshmi, P. (2021) · Sensor Review · doi:10.1108/sr-01-2021-0004 · Cited by 6
Sports and Health Management Using Big Data Based on Voice Feature Processing and Internet of Things
Sun, L., & Li, M. (2021) · Scientific Programming · doi:10.1155/2021/3271863 · Cited by 6
Speech Intelligence Using Machine Learning for Aphasia Individual
Jothi, K. R., Mamatha, V. L., Saravana Balaji, B., & Yawalkar, P. J. (2019) · International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) · doi:10.1109/iccike47802.2019.9004244 · Cited by 5
Research on the application of intelligent speech recognition technology in medical big data fog computing system
Qin, B. (2021) · Journal of Decision System · doi:10.1080/12460125.2021.1980943 · Cited by 5
Speech Confusion Index (Ø): A Recognition Rate Indicator for Dysarthric Speakers
Kayasith, P., Theeramunkong, T., & Thubthong, N. (2006) · Lecture notes in computer science · doi:10.1007/11816508_60 · Cited by 4
Interactive Cognitive Training Tool Designed for Autism Spectrum Disorder Children
Liu, Y., Zuo, S., & Hsu, C.-L. (2021) · Sensors and Materials · doi:10.18494/sam.2021.3024 · Cited by 4
Self-citation
Classification of Depression and Other Psychiatric Conditions Using Speech Features Extracted from a Thai Psychiatric and Verbal Screening Test
Klangpornkun, N., Ruangritchai, M., Munthuli, A., Onsuwan, C., Jaisin, K., Pattanaseri, K., Lortrakul, J., Thanakulakkarachai, P., Anansiripinyo, T., Amornlaksananon, A., et al. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629571 · Cited by 4
Waveform Analysis and Feature Extraction from Speech Data of Dysarthric Persons
Balaji, V., & Sadashivappa, G. (2019) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin.2019.8711768 · Cited by 4
Dr. “Multi-Task”: Using Speech to Build Up Electronic Medical Records While Caring for Patients
Shagoury, J. (2010) · Springer eBooks · doi:10.1007/978-1-4419-5951-5_11 · Cited by 4
Detecting and Analysing Spontaneous Oral Cancer Speech in the Wild
Halpern, B. M., Son, R. V., Brekel, M. V. D., & Scharenborg, O. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-1598 · Cited by 4
Speech interfacing for diagnosis reporting systems: an overview
de Bruijn, L. M., Verheijen, E. N. G., Hasman, A., van Nes, F. L., & Arends, J. W. (1995) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(95)01676-k · Cited by 3
Transcription makeover. Virginia's Rockingham Memorial Hospital improves its clinical documentation process by implementing advanced speech-recognition technologies.
Rozmus, M. (2010) · PubMed · Cited by 3
An Utterance Verification System for Word Naming Therapy in Aphasia
Barbera, D. S., Huckvale, M. A., Fleming, V., Upton, E., Coley-Fisher, H., Shaw, I., Latham, W., Leff, A. P., & Crinion, J. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2265 · Cited by 3
Making a Distinction Between Schizophrenia and Bipolar Disorder Based on Temporal Parameters in Spontaneous Speech
Gosztolya, G., Bagi, A., Szalóki, S., Szendi, I., & Hoffmann, I. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-0049 · Cited by 3

Research works printed but not linked: 11

Rabiner, 1989, A tutorial on hidden Markov models and selected applications in speech recognition, P, IEEE ASME Trans. Mechatron., 77, 257
Teichgraber, 1999, Automatic speech recognition for report generation in computed tomography, Rofo.-Fortschr. Rontg., 171, 396
Heinzer, 2010, Essential elements of nursing notes and the transition to electronic health records: the migration from narrative charting will require creativity to include essential elements in EHRs, J. Healthc. Inf. Manag., 24, 53
Hireš, 2022, Convolutional neural network ensemble for Parkinson's disease detection from voice recordings, Comput. Biol. Med., 141, 10.1016/j.compbiomed.2021.105021 · DOI
Chang, 2021, Respiratory sound recognition of chronic obstructive pulmonary disease patients based on HHT-MFCC and short-term energy, J. Comput. Appl., 41, 598
Zhang, 2020, Construction and application of intelligent mobile ward round system based on speech recognition, Chin. J. Health Inform. And Manag., 17, 799
Lin, 2021, Application and analysis of artificial intelligence voice system in postoperative follow-up of children with congenital cataract, Eye Sci., 36, 23
Tao, 2019, Non-contact multi-channel natural interactive surgical environment under sterile conditions, J. Softw., 30, 2986
Gibson, 2021, Recommendations for telemedicine reimbursement, J. Med. Pract. Manag.: J. Med. Pract. Manag., 36, 226
Zhang, 2021, Cognitive-motion rehabilitation medical robot application design, Inf. Control, 50, 740
Hao, 2020, Design and implementation of intelligent guidance service robot, Comput. Appl. and Softw., 37, 329

Sorted by citations

AI-driven assistive technologies in inclusive education: benefits, challenges, and policy recommendations
Kooli, C., & CHAKRAOUI, R. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.101042 · Cited by 91
Transcribing in the digital age: qualitative research practice utilizing intelligent speech recognition technology
Eftekhari, H. (2024) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvae013 · Cited by 72
Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions
Mohammad‐Rahimi, H., Sohrabniya, F., Ourang, S. A., Dianat, O., Aminoshariae, A., Nagendrababu, V., Dummer, P. M. H., Duncan, H. F., & Nosrat, A. (2024) · International Endodontic Journal · doi:10.1111/iej.14128 · Cited by 74
The influence of artificial intelligence techniques on disruption management: Does supply chain dynamism matter?
Samadhiya, A., Yadav, S., Kumar, A., V, Majumdar, A., Luthra, S., Garza‐Reyes, J. A., & Upadhyay, A. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102394 · Cited by 52
Leveraging artificial intelligence in neurosurgery—unveiling ChatGPT for neurosurgical discharge summaries and operative reports
Dubinski, D., Won, S.-Y., Trnovec, S., Behmanesh, B., Baumgarten, P., Dinc, N., Konczalla, J., Chan, A., Bernstock, J. D., Freiman, T. M., et al. (2024) · Acta Neurochirurgica · doi:10.1007/s00701-024-05908-3 · Cited by 45

See all 72

Authors: Zhang, J., Wu, J., Qiu, Y., Song, A., Li, W., Li, X., & Liu, Y.
Affiliations: State Key Laboratory of Digital Medical Engineering (CN)
Guangdong Academy of Medical Sciences (CN)
Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
Corresponding author: Zhang, Jun
ORCID: Zhang, Jun
Wu, Jingyue
Song, Aiguo
Li, Weifeng
Li, Xin
Liu, Yecheng
Venue: Computers in Biology and Medicine
Publisher: Elsevier BV
Published: 2023-02
Volume: 153
Pages: 106517
ISSN: 0010-4825 (ISSN-L)
DOI: 10.1016/j.compbiomed.2022.106517
PMC: PMC9814440
OpenAlex: W4313561446
Cited by: 69
References: 220
Citations by year: 2026: 15 · 2025: 24 · 2024: 20 · 2023: 10
FWCI: 1.44
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
Locations: Computers in Biology and Medicine (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: National Key Research and Development Program of China (2020AAA0109603)
National Natural Science Foundation of China (61873066, 62173090)
Ministry of Science and Technology of the People's Republic of China
Southeast University (2242020R40096)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: speech recognition, smart hospitals, disease diagnosis, human-machine interaction, stroke rehabilitation, noise interference, pronunciation differences, medical robotics
MeSH terms: COVID-19, COVID-19 Testing, Artificial Intelligence, Humans, Robotics, Speech, Pandemics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks