Multimodal Age and Gender Estimation for Adaptive Human-Robot Interaction: A Systematic Literature Review
Open accessAbstract
Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal engagement and speech with the Human-Robot Interaction life world. The study touches on the presentation of many major concepts, namely Age Estimation, Gender Estimation, Speaker Recognition, Speech recognition, Speaker Localization, and Speaker Gender Identification. According to specific criteria, they were presented to all studies. The essay compares these themes to the thematic findings of other review studies on the same topic such as multimodal age, gender estimation, and dataset used. The main objective of this paper is to provide a comprehensive analysis based on the surveyed region. The study provides a platform for professors, researchers, and students alike, and proposes directions for future research.
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
Cites
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?
A systematic literature review was conducted using the PRISMA methodology.
“As part of this investigation, a systematic literature review was conducted using the PRISMA methodology (Supplementary Materials) [40,45,46,47,48,49,50].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
RETRACTED: Human-robot interaction in higher education for predicting student engagement
Cui, Y. J., Song, X., Hu, Q., Li, Y., Sharma, P., & Khapre, S. P. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107827 · Cited by 24 · see citing works
RetractedThe PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S. E., et al. (2020) · doi:10.31222/osf.io/v7gm2_v1 · Cited by 8,090 · see citing works
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J., Bossuyt, P., Boutron, I., Hoffmann, T., mulrow, C., Shamseer, L., Tetzlaff, J., Akl, E., Brennan, S. E., et al. (2020) · doi:10.31222/osf.io/v7gm2 · Cited by 5,177 · see citing works
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921 · see citing works
TIMIT Acoustic-Phonetic Continuous Speech Corpus
Garofolo, J. S., Lamel, L. F., Fisher, W. M., Pallett, D. S., Dahlgren, N. L., Zue, V. W., & Fiscus, J. G. (2020) · Americanae (AECID Library) · doi:10.35111/17gk-bn40 · Cited by 2,509 · see citing works
Deep learning for visual understanding: A review
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504 · see citing works
VoxCeleb2: Deep Speaker Recognition
Chung, J. S., Nagrani, A., & Zisserman, A. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-1929 · Cited by 2,256 · see citing works
VoxCeleb: A Large-Scale Speaker Identification Dataset
Nagrani, A., Chung, J. S., & Zisserman, A. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-950 · Cited by 2,210 · see citing works
Review of Deep Learning Algorithms and Architectures
Shrestha, A., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2912200 · Cited by 1,883 · see citing works
The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms
Makridakis, S. (2017) · Futures · doi:10.1016/j.futures.2017.03.006 · Cited by 1,866 · see citing works
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
Rothe, R., Timofte, R., & Gool, L. V. (2016) · International Journal of Computer Vision · doi:10.1007/s11263-016-0940-3 · Cited by 904 · see citing works
Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
Le, X.-H., Ho, H. V., Lee, G., & Jung, S. (2019) · Water · doi:10.3390/w11071387 · Cited by 893 · see citing works
Akçay, M. B., & Oğuz, K. (2019) · Speech Communication · doi:10.1016/j.specom.2019.12.001 · Cited by 743 · see citing works
A glimpsing model of speech perception in noise
Cooke, M. P. (2006) · The Journal of the Acoustical Society of America · doi:10.1121/1.2166600 · Cited by 710 · see citing works
The M4 Competition: Results, findings, conclusion and way forward
Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2018.06.001 · Cited by 683 · see citing works
Ordinal Regression with Multiple Output CNN for Age Estimation
Niu, Z., Zhou, M., Wang, L., Gao, X., & Hua, G. (2016) · 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/cvpr.2016.532 · Cited by 677 · see citing works
Emotion Recognition from Physiological Signal Analysis: A Review
Egger, M., Ley, M., & Hanke, S. (2019) · Electronic Notes in Theoretical Computer Science · doi:10.1016/j.entcs.2019.04.009 · Cited by 568 · see citing works
Graph-Based Semi-Supervised Learning: A Comprehensive Review
Song, Z., Yang, X., Xu, Z., & King, I. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3155478 · Cited by 353 · see citing works
The role of the human-robot interaction in consumers’ acceptance of humanoid retail service robots
Song, C. S., & Kim, Y. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.087 · Cited by 346 · see citing works
Service robots: Applying social exchange theory to better understand human–robot interactions
Kim, H., So, K. K. F., & Wirtz, J. (2022) · Tourism Management · doi:10.1016/j.tourman.2022.104537 · Cited by 338 · see citing works
Dargan, S., & Kumar, M. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.113114 · Cited by 329 · see citing works
Gender Biases in Estimation of Others’ Pain
Zhang, L., Losin, E. A. R., Ashar, Y. K., Koban, L., & Wager, T. D. (2021) · Journal of Pain · doi:10.1016/j.jpain.2021.03.001 · Cited by 315 · see citing works
Coronado, E., Kiyokawa, T., Ricardez, G. A. G., Ramírez-Alpizar, I. G., Venture, G., & Yamanobe, N. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.04.007 · Cited by 289 · see citing works
A hybrid deep learning CNN–ELM for age and gender classification
Duan, M., Li, K., Yang, C., & Li, K. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.08.062 · Cited by 259 · see citing works
JNAS: Japanese speech corpus for large vocabulary continuous speech recognition research.
Itou, K., Yamamoto, M., Takeda, K., Takezawa, T., Matsuoka, T., Kobayashi, T., Shikano, K., & Itahashi, S. (1999) · Journal of the Acoustical Society of Japan (E) · doi:10.1250/ast.20.199 · Cited by 249 · see citing works
Deeply-Learned Feature for Age Estimation
Wang, X., Guo, R., & Kambhamettu, C. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.77 · Cited by 218 · see citing works
Online Stability in Human-Robot Cooperation with Admittance Control
Dimeas, F., & Aspragathos, N. (2016) · IEEE Transactions on Haptics · doi:10.1109/toh.2016.2518670 · Cited by 207 · see citing works
A survey of emotion recognition methods with emphasis on E-Learning environments
Imani, M., & Montazer, G. A. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102423 · Cited by 190 · see citing works
Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning
Cummins, N., Baird, A., & Schuller, B. W. (2018) · Methods · doi:10.1016/j.ymeth.2018.07.007 · Cited by 182 · see citing works
Sethi, D., Bharti, S., & Prakash, C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102314 · Cited by 182 · see citing works
Automated emotion recognition: Current trends and future perspectives
Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Barua, P. D., Murugappan, M., Chakole, Y., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106646 · Cited by 172 · see citing works
Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102447 · Cited by 171 · see citing works
Convolutional RNN: An enhanced model for extracting features from sequential data
Keren, G., & Schuller, B. W. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727636 · Cited by 166 · see citing works
A survey of speech emotion recognition in natural environment
Fahad, M. S., Ranjan, A., Yadav, J., & Deepak, A. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102951 · Cited by 162 · see citing works
Multimodal Video Sentiment Analysis Using Deep Learning Approaches, a Survey
Abdu, S. A., Yousef, A. H., & Salem, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.06.003 · Cited by 147 · see citing works
Multimodality in the English language classroom: A systematic review of literature
Lim, F. V., Toh, W., & Nguyen, T. T. H. (2022) · Linguistics and Education · doi:10.1016/j.linged.2022.101048 · Cited by 122 · see citing works
A Survey on Evaluation Metrics for Machine Translation
Lee, S., Lee, J., Moon, H., Park, C., Seo, J., Eo, S., Koo, S., & Lim, H. (2023) · Mathematics · doi:10.3390/math11041006 · Cited by 122 · see citing works
Do RNN and LSTM have Long Memory?Preprint
Zhao, J., Huang, F., Lv, J., Duan, Y., Qin, Z., Li, G., & Tian, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03860 · Cited by 118 · see citing works
Forecasting stochastic neural network based on financial empirical mode decomposition
Wang, J., & Wang, J. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.03.004 · Cited by 109 · see citing works
Aafaq, N., Mian, A. S., Liu, W., Gilani, S. Z., & Shah, M. (2018) · ACM Computing Surveys · doi:10.1145/3355390 · Cited by 109 · see citing works
Positive technology for elderly well-being: A review
Grossi, G., Lanzarotti, R., Napoletano, P., Noceti, N., & Odone, F. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.03.016 · Cited by 105 · see citing works
Systematic Literature Review of Sampling Process in Rapidly-Exploring Random Trees
Veras, L. G. D. O., Medeiros, F. L. L., & Guimarães, L. N. F. (2019) · IEEE Access · doi:10.1109/access.2019.2908100 · Cited by 104 · see citing works
Moving Window Regression: A Novel Approach to Ordinal Regression
Shin, N.-H., Lee, S., & Kim, C. (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/cvpr52688.2022.01820 · Cited by 101 · see citing works
Evaluating the intelligibility benefit of speech modifications in known noise conditions
Cooke, M. P., Mayo, C., Valentini-Botinhao, C., Stylianou, Y., Sauert, B., & Tang, Y. M. (2013) · Speech Communication · doi:10.1016/j.specom.2013.01.001 · Cited by 99 · see citing works
Antipov, G., Baccouche, M., Berrani, S.-A., & Dugelay, J. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.105 · Cited by 96 · see citing works
A framework for joint estimation of age, gender and ethnicity on a large database
Guo, G., & Mu, G. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.04.011 · Cited by 95 · see citing works
Facial Age Estimation With Age Difference
Hu, Z., Wen, Y., Wang, J., Wang, M., Hong, R., & Yan, S. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2633868 · Cited by 94 · see citing works
Review of Interfaces for Industrial Human-Robot Interaction
Berg, J., & Lu, S. (2020) · Current Robotics Reports · doi:10.1007/s43154-020-00005-6 · Cited by 92 · see citing works
Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks
Zazo, R., Nidadavolu, P. S., Chen, N., González-Rodríguez, J., & Dehak, N. (2018) · IEEE Access · doi:10.1109/access.2018.2816163 · Cited by 91 · see citing works
Multimodality in meta-learning: A comprehensive survey
Ma, Y., Zhao, S., Wang, W., Li, Y., & King, I. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108976 · Cited by 89 · see citing works
Label-Sensitive Deep Metric Learning for Facial Age Estimation
Liu, H., Lu, J., Feng, J., & Zhou, J. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2746062 · Cited by 84 · see citing works
Hayder, I. M., Al-Amiedy, T. A., Ghaban, W., Saeed, F., Nasser, M., Al-Ali, G. A., & Younis, H. A. (2023) · Processes · doi:10.3390/pr11020481 · Cited by 84 · see citing works
Self-citationA Database of Age and Gender Annotated Telephone Speech
Burkhardt, F., Eckert, M., Johannsen, W., & Stegmann, J. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2mgazc5z3h3u · Cited by 81 · see citing works
A survey of multidisciplinary domains contributing to affective computing
Arya, R., Singh, J., & Kumar, A. P. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100399 · Cited by 77 · see citing works
On the use of DAG-CNN architecture for age estimation with multi-stage features fusion
Taheri, S., & Toygar, Ö. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.071 · Cited by 74 · see citing works
Synteza i aktywność biologiczna nowych analogów tiosemikarbazonowych chelatorów żelaza
Serda, M. (2013) · Cited by 73 · see citing works
Speech Recognition Using Articulatory and Excitation Source Features
Rao, K. S., & Manjunath, K. E. (2017) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-319-49220-9 · Cited by 71 · see citing works
Speaker age estimation using i-vectors
Bahari, M. H., McLaren, M., hamme, H. V., & van Leeuwen, D. A. (2014) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2014.05.003 · Cited by 70 · see citing works
Human-robot Collaboration Demonstrator Combining Speech Recognition and Haptic Control
Gustavsson, P., Syberfeldt, A., Brewster, R., & Wang, L. (2017) · Procedia CIRP · doi:10.1016/j.procir.2017.03.126 · Cited by 65 · see citing works
Deep Learning-based Multimodal Control Interface for Human-Robot Collaboration
Liu, H., Fang, T., Zhou, T.-Y., Wang, Y., & Wang, L. (2018) · Procedia CIRP · doi:10.1016/j.procir.2018.03.224 · Cited by 65 · see citing works
Brain aging and speech perception: Effects of background noise and talker variability
Tremblay, P., Brisson, V., & Deschamps, I. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117675 · Cited by 57 · see citing works
Minimum distance calculation using skeletal tracking for safe human-robot interaction
Secil, S., & Özkan, M. (2021) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2021.102253 · Cited by 57 · see citing works
End-to-end Deep Neural Network Age Estimation
Ghahremani, P., Nidadavolu, P. S., Chen, N., Villalba, J., Povey, D., Khudanpur, S. P., & Dehak, N. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-2015 · Cited by 52 · see citing works
Perceiving the person and their interactions with the others for social robotics – A review
Tapus, A., Bandera, A. J., Vázquez-Martín, R., & Calderita, L. V. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.03.006 · Cited by 49 · see citing works
CT and MR imaging used in age estimation: a systematic review.
Bjørk, M. B., & Kvaal, S. I. (2018) · Europe PMC (PubMed Central) · Cited by 47 · see citing works
Cao, Y. T., & Daumé, H. (2021) · Computational Linguistics · doi:10.1162/coli_a_00413 · Cited by 47 · see citing works
Kock, A. B., & Timo Teräsvirta, Y. (2015) · Econometric Reviews · doi:10.1080/07474938.2015.1035163 · Cited by 46 · see citing works
Noise and Tremor in the Perception of Vocal Aging in Males
Harnsberger, J. D., Brown, W. S., Shrivastav, R., & Rothman, H. B. (2009) · Journal of Voice · doi:10.1016/j.jvoice.2009.01.003 · Cited by 45 · see citing works
Paliga, M., & Pollak, A. M. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102698 · Cited by 44 · see citing works
Speaker Age Classification and Regression Using i-Vectors
Grzybowska, J., & Kacprzak, S. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1118 · Cited by 43 · see citing works
A Review on Voice-based Interface for Human-Robot Interaction
Badr, A. A., & Hassan, A. K. A. (2020) · Iraqi Journal for Electrical And Electronic Engineering · doi:10.37917/ijeee.16.2.10 · Cited by 38 · see citing works
Self-citationChen, J., & Ro, P. I. (2022) · Mechatronics · doi:10.1016/j.mechatronics.2022.102802 · Cited by 38 · see citing works
CyTex: Transforming speech to textured images for speech emotion recognition
Bakhshi, A., Harimi, A., & Chalup, S. K. (2022) · Speech Communication · doi:10.1016/j.specom.2022.02.007 · Cited by 37 · see citing works
Automatic speaker profiling from short duration speech data
Kalluri, S. B., Vijayasenan, D., & Ganapathy, S. (2020) · Speech Communication · doi:10.1016/j.specom.2020.03.008 · Cited by 33 · see citing works
GRA-GAN: Generative adversarial network for image style transfer of Gender, Race, and age
Kim, Y. H., Nam, S. H., Hong, S. B., & Park, K. R. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116792 · Cited by 33 · see citing works
Speaker verification in score-ageing-quality classification space
Kelly, F., Drygajlo, A., & Harte, N. (2013) · Computer Speech & Language · doi:10.1016/j.csl.2012.12.005 · Cited by 32 · see citing works
Zhao, T. C., & Kuhl, P. K. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119242 · Cited by 32 · see citing works
Speaker verification with long-term ageing data
Kelly, F., Drygajlo, A., & Harte, N. (2012) · doi:10.1109/icb.2012.6199796 · Cited by 30 · see citing works
Kalluri, S. B., Vijayasenan, D., & Ganapathy, S. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683397 · Cited by 30 · see citing works
Zhang, Q., Li, F., Zhang, Q., & Xiong, C. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.113 · Cited by 29 · see citing works
Minematsu, Sekiguchi, & Hirose (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2002.1005695 · Cited by 28 · see citing works
Results of the n-best 2008 dutch speech recognition evaluation
van Leeuwen, D. A., Kessens, J. M., Sanders, E., & van den Heuvel, H. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-677 · Cited by 27 · see citing works
Analysis of Race and Gender Bias in Deep Age Estimation Models
Andraz, P., Štruc, V., & Grm, K. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287219 · Cited by 27 · see citing works
Analyzing features for automatic age estimation on cross-sectional data
Spiegl, W., Stemmer, G., Lasarcyk, E., Kolhatkar, V., Cassidy, A. S., Potard, B., Shum, S. H., Song, Y. C., Xu, P., Beyerlein, P., et al. (2009) · Interspeech (USB) · doi:10.21437/interspeech.2009-740 · Cited by 25 · see citing works
Speaker age estimation using Hidden Markov Model weight supervectors
Bahari, M. H., & hamme, H. V. (2012) · International Conference on Information Science, Signal Processing and their Applications (ISSPA) · doi:10.1109/isspa.2012.6310606 · Cited by 25 · see citing works
Asif, M. K., Nambiar, P., Ibrahim, N., Al-Amery, S. M., & Khan, I. M. (2019) · Legal Medicine · doi:10.1016/j.legalmed.2019.05.003 · Cited by 24 · see citing works
Towards Speaker Age Estimation With Label Distribution Learning
Si, S., Wang, J., Peng, J., & Xiao, J. Z. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746378 · Cited by 24 · see citing works
Toward Gender-Inclusive Coreference Resolution
Cao, Y. T., & Daumé, H., III (2020) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/2020.acl-main.418 · Cited by 22 · see citing works
Survey of Robotics in Education, Taxonomy, Applications, and Platforms during COVID-9
Younis, H. A., Mohamed, A. S. A., Jamaludin, R., & Wahab, M. N. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.013746 · Cited by 22 · see citing works
Self-citationSpeech Interaction to Control a Hands-Free Delivery Robot for High-Risk Health Care Scenarios
Grasse, L., Boutros, S. J., & Tata, M. S. (2021) · Frontiers in Robotics and AI · doi:10.3389/frobt.2021.612750 · Cited by 22 · see citing works
Age and gender estimation using deep residual learning network
Lee, S. H., Hosseini, S., Kwon, H. J., Moon, J., Koo, H. I., & Cho, N. I. (2018) · International Workshop on Advanced Imaging Technology (IWAIT) · doi:10.1109/iwait.2018.8369763 · Cited by 20 · see citing works
Lileikyté, R., Irvin, D., & Hansen, J. H. L. (2022) · Speech Communication · doi:10.1016/j.specom.2022.01.006 · Cited by 20 · see citing works
Short-term analysis for estimating physical parameters of speakers
Singh, R., Raj, B., & Baker, J. (2016) · doi:10.1109/iwbf.2016.7449696 · Cited by 19 · see citing works
Age-VOX-Celeb: Multi-Modal Corpus for Facial and Speech Estimation
Tawara, N., Ogawa, A., Kitagishi, Y., & Kamiyama, H. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414272 · Cited by 19 · see citing works
Brain age estimation using multi-feature-based networks
Liu, X., Beheshti, I., Zheng, W., Li, Y., Li, S., Zhao, Z., Yao, Z., & Hu, B. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105285 · Cited by 19 · see citing works
Estimation of the glottal source from coded telephone speech using deep neural networks
Narendra, N. P., Airaksinen, M., Story, B. H., & Alku, P. (2018) · Speech Communication · doi:10.1016/j.specom.2018.12.002 · Cited by 18 · see citing works
Lele, A. (2018) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-3384-2_8 · Cited by 18 · see citing works
Speech recognition using HMM and Soft Computing
Srivastava, R., & Pandey, D. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.10.097 · Cited by 17 · see citing works
Shahnawazuddin, S., Adiga, N., Kathania, H. K., Pradhan, G., & Sinha, R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2018.05.003 · Cited by 16 · see citing works
Predicting reaction based on customer's transaction using machine learning approaches
Hayder, I. M., Al-Ali, G. A., & Younis, H. A. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i1.pp1086-1096 · Cited by 16 · see citing works
Self-citationAutomatic intelligibility assessment of dysarthric speech using glottal parameters
Narendra, N. P., & Alku, P. (2020) · Speech Communication · doi:10.1016/j.specom.2020.06.003 · Cited by 15 · see citing works
Götz, S. (2018) · Proceedings of the 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings · doi:10.1145/3270112.3270117 · Cited by 14 · see citing works
Age estimation in foreign-accented speech by non-native speakers of English
Jiao, D., Watson, V., Wong, S., Gnevsheva, K., & Nixon, J. S. (2018) · Speech Communication · doi:10.1016/j.specom.2018.12.005 · Cited by 12 · see citing works
Zeng, J., Peng, J., & Zhao, Y. (2019) · Applied Acoustics · doi:10.1016/j.apacoust.2019.107096 · Cited by 12 · see citing works
Younis, H. A., Jamaludin, R., Wahab, M. N. A., & Mohamed, A. S. A. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/928/3/032014 · Cited by 12 · see citing works
Self-citationLi, H., Schrode, K. M., & Bee, M. A. (2022) · Animal Behaviour · doi:10.1016/j.anbehav.2022.05.003 · Cited by 10 · see citing works
Multi-layer age regression for face age estimation
Ng, C.-C., Cheng, Y.-T., Hsu, G.-S. J., & Yap, M. H. (2017) · Machine Vision Applications · doi:10.23919/mva.2017.7986859 · Cited by 9 · see citing works
Automatic age-group estimation from gait energy images
Abirami, B., Subashini, T. S., & Mahavaishnavi, V. (2020) · Materials Today Proceedings · doi:10.1016/j.matpr.2020.08.298 · Cited by 9 · see citing works
Estimating Age in Short Utterances Based on Multi-Class Classification Approach
Badr, A. A., & Hassan, A. K. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.016732 · Cited by 9 · see citing works
Self-citationLee, K. H., Baek, S. G., Lee, H. J., Lee, S. H., & Koo, J. C. (2021) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2021.103916 · Cited by 9 · see citing works
Younis, H. A., Mohamed, A. S. A., Wahab, M. N. A., Jamaludin, R., & Salisu, S. (2021) · International Conference on Communication & Information Technology (ICICT) · doi:10.1109/icict52195.2021.9568457 · Cited by 9 · see citing works
Self-citationSpeech Enhancement Using a Risk Estimation Approach
Sadasivan, J., Seelamantula, C. S., & Muraka, N. R. (2019) · Speech Communication · doi:10.1016/j.specom.2019.11.001 · Cited by 8 · see citing works
Matsui, T., Irino, T., Uemura, R., Yamamoto, K., Kawahara, H., & Patterson, R. D. (2021) · Speech Communication · doi:10.1016/j.specom.2021.10.006 · Cited by 8 · see citing works
Age Estimation in Short Speech Utterances Based on Bidirectional Gated-Recurrent Neural Networks
Badr, A. A., & Hassan, A. K. A. (2021) · Engineering and Technology Journal · doi:10.30684/etj.v39i1b.1905 · Cited by 7 · see citing works
Self-citationEstimation of gender and age using CNN-based face recognition algorithm
Yeon, L. S. (2020) · International journal of advanced smart convergence · doi:10.7236/ijasc.2020.9.2.203 · Cited by 7 · see citing works
Neural decoding of speech with semantic-based classification
Lin, Y., & Hsieh, P. (2022) · Cortex · doi:10.1016/j.cortex.2022.05.018 · Cited by 7 · see citing works
Ilyas, M., & Naït‐Ali, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.11.016 · Cited by 6 · see citing works
de Sousa, A. L. A., da Silva, B. A. K., Lopes, S. L. P. D. C., Mendes, J. D. P., Pinto, P. H. V., & Pinto, A. S. B. (2022) · Forensic Imaging · doi:10.1016/j.fri.2022.200489 · Cited by 5 · see citing works
Multi-way regression for age prediction exploiting speech and face image information
Pantraki, E., & Kotropoulos, C. L. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081599 · Cited by 4 · see citing works
Adversarial Spatial Frequency Domain Critic Learning for Age and Gender Classification
Lee, S. S., Kim, H. G., Kim, K., & Ro, Y. M. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451616 · Cited by 4 · see citing works
Estimation of age from speech using excitation source features
Avikal, S., Sharma, K. S., Barthwal, A., Kumar, K. C. N., & Badhotiya, G. K. (2021) · Materials Today Proceedings · doi:10.1016/j.matpr.2021.02.159 · Cited by 3 · see citing works
Towards Speaker Age Estimation with Label Distribution LearningPreprint
Si, S., Wang, J., Peng, J., et al. (2022) · doi:10.31219/osf.io/jtcxu · Cited by 3 · see citing works
Hierarchical Attention-based Age Estimation and Bias EstimationPreprint
Hiba, S., & Keller, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.09882 · Cited by 3 · see citing works
Hayamizu, S., Itahashi, S., Kobayashi, T., & Takezawa, T. (2002) · Proceedings of the Annual Conference of JSAI Proceedings of the 16th Annual Conserence of JSAI, 2002 · doi:10.11517/pjsai.jsai02.0.194.0 · Cited by 1 · see citing works
Tang, Y. M. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101220 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 8
- Badr, 2022, Speaker gender identification in matched and mismatched conditions based on stacking ensemble method, J. Eng. Sci. Technol., 17, 1119
- 2014, How to do a structured literature review in computer science, Researchgate, 1, 1
- Keele, S. (2023, February 12). Guidelines for Performing Systematic Literature Reviews in Software Engineering. Tech. Report, Ver. 2.3 EBSE Tech. Report. EBSE. Available online: https://www.elsevier.com/__data/promis_misc/525444systematicreviewsguide.pdf.
- Mcmahan, H.B., Ramage, D., and Com, B.G. (2012). Federated Learning of Deep Networks using Model Averaging. arXiv.
- Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press.
- Liu, Y., Fung, P., Yang, Y., Cieri, C., Huang, S., and Graff, D. (2006). Lecture Notes in Computer Science, Springer.
- Zhao, M., Ma, Y., Liu, M., and Xu, M. (2021). The speakin system for voxceleb speaker recognition challange 2021. arXiv.
- Sadjadi, S.O. (2021). NIST SRE CTS Superset: A large-scale dataset for telephony speaker recognition. arXiv.
Cited by
Sorted by citations
Younis, H. A., Eisa, T. A. E., Nasser, M., Sahib, T. M., Noor, A. A., Alyasiri, O. M., Salisu, S., Hayder, I. M., & Younis, H. A. (2024) · Diagnostics · doi:10.3390/diagnostics14010109 · Cited by 242 · see citing works
Self-citationYounis, H. A., Ruhaiyem, N. I. R., Ghaban, W., Gazem, N. A., & Nasser, M. (2023) · Electronics · doi:10.3390/electronics12132864 · Cited by 72 · see citing works
Self-citationYounis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Eisa, T. A. E., Nasser, M., Tan, T.-P., Samsudin, N. H., & Salisu, S. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106511 · Cited by 6 · see citing works
Self-citationRobots in the Wild: Contextually-Adaptive Human-Robot Interactions in Urban Public Environments
Yu, X., Wang, Y., Tran, T. T. M., Zhao, Y., Berrío, J. S., Hoggenmüeller, M., Humphry, J., Loke, L., Masuda, L., Parker, C., et al. (2023) · Australian Conference on Computer-Human Interaction (OzCHI) · doi:10.1145/3638380.3638440 · Cited by 3 · see citing works
Priadana, A., Nguyen, D.-L., Vo, X.-T., Uddin, R. M. A., & Jo, K.-H. (2024) · Conference on Human System Interactions · doi:10.1109/hsi61632.2024.10613546 · Cited by 3 · see citing works
Younis, H. A., Ruhaiyem, N. I. R., Samsudin, T.-P., Samsudin, N. H., Eisa, T. A. E., Badr, A. A., Nasser, M., & Salisu, S. (2023) · Research Square · doi:10.21203/rs.3.rs-3502219/v1 · Cited by 2 · see citing works
Self-citationMulti-Modal Fusion for Enhanced Image and Speech Recognition in AI Systems
Tak, A. (2021) · International Journal of Science and Research (IJSR) · doi:10.21275/sr231208202748 · Cited by 1 · see citing works
Ren, J., Meng, H., Dai, S., Tang, Y., Wu, D., Yang, H., Zhan, X., Zhang, X., Guo, Y.-C., & Guo, Y. (2025) · International Journal of Legal Medicine · doi:10.1007/s00414-025-03674-0 · Cited by 1 · see citing works
G, M. S. N., B, P. R., R, S. C., Vaishnavi, T., & Varsha, V. (2026) · International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE) · doi:10.1109/iitcee67948.2026.11394555 · Cited by 1 · see citing works
Younis, H. A., Hayder, I. M., Salisu, S., Muthmainnah, M., Shahid, M., & Younis, H. A. (2024) · International Journal of Education Research and Development · doi:10.52760/ijerd.v4i1.54 · Cited by 1 · see citing works
Self-citationUnA-Mix: Rethinking Image Mixtures for Unsupervised Person Re-Identification
Liu, J., Sun, H., Liu, W., Guo, A., & Zhang, J. (2024) · Processes · doi:10.3390/pr12010168
Accurate Age and Gender Prediction Using DNN Model from Real World Camera Feeds
Jayabharathi, K. (2025) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v10i31s.5112
Kavya, V., & Raj, D. (2025) · AIP conference proceedings · doi:10.1063/5.0277803
Predicting Future Gold Prices Using the Feedback-Matching Algorithm
Harif, A. H., Hassan, A. K. A., & Nuaimy, L. A. (2025) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk67458.2025.11207048
A Rubber-sheet Transformation Model for Personalized Human-Robot Proxemics
Camara, F., Jabaoui, A. E., Mukundan, R., & Obaid, M. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246922
Utilizing Machine Learning Algorithms and SMOTE for Analyzing and Predicting Homicides
Younis, H. A., Al-Ali, G. A., Hayder, I. M., Salisu, S., & Nasser, M. (2025) · International Journal of Mechatronics Robotics and Artificial Intelligence · doi:10.33971/ijmrai.1.1.5
Self-citationDesigning Adaptive Multimodal Interaction Frameworks to Enhance Human-AI Collaboration
Gong, W., & Chen, X. (2026) · Journal of Visualized Experiments · doi:10.3791/69830
A CNN–NCP Based Hybrid Deep Learning Model for Speech-Driven Gender Classification
Olgun, S., Balım, C., & Olgun, N. (2026) · Bitlis Eren Üniversitesi Fen Bilimleri Dergisi · doi:10.17798/bitlisfen.1803512
Zhao, J. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3671
Sun, L., & Zhou, Z. (2026) · International Journal of Humanoid Robotics · doi:10.1142/s0219843626400153
Perumal, M., Mia, M. S., Mahajan, M., Nath, N., Puri, A., & Mehta, P. K. (2026) · International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI) · doi:10.1109/icscai68849.2026.11648635
- Authors
- Younis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., Binjumah, W. M., Altuwaijri, E. A., & Nasser, M.
- Affiliations
- University of Basrah (IQ)
- Universiti Sains Malaysia (MY)
- Middle Technical University (IQ)
- Department of Computer Science, University of Technology, Baghdad 10011, Iraq
- Taibah University (SA)
- King Saud University (SA)
- Corresponding author
- Ruhaiyem, Nur Intan Raihana
- ORCID
- Venue
- Processes
- Publisher
- MDPI AG
- Published
- 2023-05-15
- Volume
- 11
- Issue
- 5
- Pages
- 1488
- ISSN
- 2227-9717 (ISSN-L)
- OpenAlex
- W4381792814
- Cited by
- 21
- References
- 123
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 5 · 2023: 3 · 2021: 1
- FWCI
- 0.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Processes (publishedVersion, cc-by)
- Funders
- Fundamental Research (FRGS/1/2021/ICT04/USM/02/1/)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- age estimation, gender prediction, human-robot interaction, adaptive learning, speaker recognition, speech recognition, speaker localization
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
