{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:28:56Z","timestamp":1781188136903,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Support Program for Scientific and Technological Innovation Teams in Universities in Henan Province","award":["24IRTSTHN028"],"award-info":[{"award-number":["24IRTSTHN028"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender\u2013emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC.<\/jats:p>","DOI":"10.3390\/biomimetics9060360","type":"journal-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T10:42:34Z","timestamp":1718361754000},"page":"360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Gender-Driven English Speech Emotion Recognition with Genetic Algorithm"],"prefix":"10.3390","volume":"9","author":[{"given":"Liya","family":"Yue","sequence":"first","affiliation":[{"name":"Fanli Business School, Nanyang Institute of Technology, Nanyang 473004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer and Software, Nanyang Institute of Technology, Nanyang 473004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiulong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Fanli Business School, Nanyang Institute of Technology, Nanyang 473004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bhushan, B. (2023, January 28\u201329). Optimal Feature Learning for Speech Emotion Recognition\u2014A DeepNet Approach. Proceedings of the 2023 International Conference on Data Science and Network Security (ICDSNS), Tiptur, India.","DOI":"10.1109\/ICDSNS58469.2023.10245147"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"47795","DOI":"10.1109\/ACCESS.2021.3068045","article-title":"A comprehensive review of speech emotion recognition systems","volume":"9","author":"Wani","year":"2021","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.55195\/jscai.1214312","article-title":"CREMA-D: Improving Accuracy with BPSO-Based Feature Selection for Emotion Recognition Using Speech","volume":"3","author":"Donuk","year":"2022","journal-title":"J. Soft Comput. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102951","DOI":"10.1016\/j.dsp.2020.102951","article-title":"A survey of speech emotion recognition in natural environment","volume":"110","author":"Fahad","year":"2021","journal-title":"Digit. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.specom.2019.12.001","article-title":"Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers","volume":"116","year":"2020","journal-title":"Speech Commun."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Issa, D., Demirci, M.F., and Yazici, A. (2020). Speech emotion recognition with deep convolutional neural networks. Biomed. Signal Process. Control, 59.","DOI":"10.1016\/j.bspc.2020.101894"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hu, G., Zhong, J., Wang, X., and Wei, G. (2022). Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study. Comput. Biol. Med., 151.","DOI":"10.1016\/j.compbiomed.2022.106239"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Barrera-Garc\u00eda, J., Cisternas-Caneo, F., Crawford, B., G\u00f3mez S\u00e1nchez, M., and Soto, R. (2023). Feature Selection Problem and Metaheuristics: A Systematic Literature Review about Its Formulation, Evaluation and Applications. Biomimetics, 9.","DOI":"10.3390\/biomimetics9010009"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108736","DOI":"10.1016\/j.asoc.2022.108736","article-title":"Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection","volume":"121","author":"Hu","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1007\/s10462-023-10567-4","article-title":"Crayfish optimization algorithm","volume":"56","author":"Jia","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"115665","DOI":"10.1016\/j.eswa.2021.115665","article-title":"Remora optimization algorithm","volume":"185","author":"Jia","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"116238","DOI":"10.1016\/j.cma.2023.116238","article-title":"LCAHA: A hybrid artificial hummingbird algorithm with multi-strategy for engineering applications","volume":"415","author":"Hu","year":"2023","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"122200","DOI":"10.1016\/j.eswa.2023.122200","article-title":"Electric eel foraging optimization: A new bio-inspired optimizer for engineering applications","volume":"238","author":"Zhao","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wu, D., Jia, H., Abualigah, L., Xing, Z., Zheng, R., Wang, H., and Altalhi, M. (2022). Enhance teaching-learning-based optimization for tsallis-entropy-based feature selection classification approach. Processes, 10.","DOI":"10.3390\/pr10020360"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1051\/meca\/2023031","article-title":"Multi-objective shape optimization of developable B\u00e9zier-like surfaces using non-dominated sorting genetic algorithm","volume":"24","author":"Lu","year":"2023","journal-title":"Mech. Ind."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1111\/1752-1688.13182","article-title":"Assessment of water resources carrying capacity using chaotic particle swarm genetic algorithm","volume":"60","author":"Gao","year":"2024","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6101","DOI":"10.1007\/s10462-022-10328-9","article-title":"A survey on binary metaheuristic algorithms and their engineering applications","volume":"56","author":"Pan","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yue, L., Hu, P., Chu, S.C., and Pan, J.S. (2023). Genetic Algorithm for High-Dimensional Emotion Recognition from Speech Signals. Electronics, 12.","DOI":"10.3390\/electronics12234779"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108964","DOI":"10.1016\/j.asoc.2022.108964","article-title":"A correlation guided genetic algorithm and its application to feature selection","volume":"123","author":"Zhou","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17410","DOI":"10.1007\/s10489-021-03048-0","article-title":"Improvement and application of hybrid real-coded genetic algorithm","volume":"52","author":"Song","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"36602","DOI":"10.1109\/ACCESS.2020.2971060","article-title":"A hybrid genetic algorithm based on information entropy and game theory","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"1299","article-title":"Application of Improved Genetic Algorithm in Function Optimization","volume":"35","author":"Yan","year":"2019","journal-title":"J. Inf. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11042-023-15518-3","article-title":"Zigzag mutation: A new mutation operator to improve the genetic algorithm","volume":"82","author":"Harifi","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1080\/01630561003656268","article-title":"Multistage markov chain modeling of the genetic algorithm and convergence results","volume":"31","author":"Dorea","year":"2010","journal-title":"Numer. Funct. Anal. Optim."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6780","DOI":"10.1016\/j.ijleo.2013.05.101","article-title":"An analysis on convergence and convergence rate estimate of elitist genetic algorithms in noisy environments","volume":"124","author":"Li","year":"2013","journal-title":"Optik"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1007\/s12555-011-0022-9","article-title":"A new fuzzy adaptive simulated annealing genetic algorithm and its convergence analysis and convergence rate estimation","volume":"12","author":"Peng","year":"2014","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/TETC.2013.2274797","article-title":"Gender-driven emotion recognition through speech signals for ambient intelligence applications","volume":"1","author":"Bisio","year":"2013","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1109\/TAFFC.2021.3071503","article-title":"Exploring the contextual factors affecting multimodal emotion recognition in videos","volume":"14","author":"Bhattacharya","year":"2021","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zaman, S.R., Sadekeen, D., Alfaz, M.A., and Shahriyar, R. (2021, January 12\u201316). One source to detect them all: Gender, age, and emotion detection from voice. Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC51774.2021.00055"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Verma, D., Mukhopadhyay, D., and Mark, E. (2016, January 12\u201313). Role of gender influence in vocal Hindi conversations: A study on speech emotion recognition. Proceedings of the 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India.","DOI":"10.1109\/ICCUBEA.2016.7860021"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5765760","DOI":"10.1155\/2023\/5765760","article-title":"Stressed Speech Emotion Recognition Using Teager Energy and Spectral Feature Fusion with Feature Optimization","volume":"2023","author":"Bandela","year":"2023","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rituerto-Gonz\u00e1lez, E., M\u00ednguez-S\u00e1nchez, A., Gallardo-Antol\u00edn, A., and Pel\u00e1ez-Moreno, C. (2019). Data augmentation for speaker identification under stress conditions to combat gender-based violence. Appl. Sci., 9.","DOI":"10.3390\/app9112298"},{"key":"ref_33","unstructured":"Kaggle (2024, June 12). Speech Emotion Recognition for Emergency Calls. Available online: https:\/\/www.kaggle.com\/datasets\/anuvagoyal\/speech-emotion-recognition-for-emergency-calls."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10579-008-9076-6","article-title":"IEMOCAP: Interactive emotional dyadic motion capture database","volume":"42","author":"Busso","year":"2008","journal-title":"Lang. Resour. Eval."},{"key":"ref_35","first-page":"1288","article-title":"Comparative Wavelet and MFCC Speech Emotion Recognition Experiments on the RAVDESS Dataset","volume":"71","author":"Bajaj","year":"2022","journal-title":"Math. Stat. Eng. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Mengash, H.A., Alruwais, N., Kouki, F., Singla, C., Abd Elhameed, E.S., and Mahmud, A. (2023). Archimedes Optimization Algorithm-Based Feature Selection with Hybrid Deep-Learning-Based Churn Prediction in Telecom Industries. Biomimetics, 9.","DOI":"10.3390\/biomimetics9010001"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yao, L., Yang, J., Yuan, P., Li, G., Lu, Y., and Zhang, T. (2023). Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection. Biomimetics, 8.","DOI":"10.3390\/biomimetics8060492"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"462","DOI":"10.4218\/etrij.2020-0458","article-title":"Speech emotion recognition based on genetic algorithm\u2013decision tree fusion of deep and acoustic features","volume":"44","author":"Sun","year":"2022","journal-title":"ETRI J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.asoc.2017.03.013","article-title":"Hybrid BBO_PSO and higher order spectral features for emotion and stress recognition from natural speech","volume":"56","author":"Yogesh","year":"2017","journal-title":"Appl. Soft Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"14463","DOI":"10.1007\/s00521-022-07261-x","article-title":"GRaNN: Feature selection with golden ratio-aided neural network for emotion, gender and speaker identification from voice signals","volume":"34","author":"Garain","year":"2022","journal-title":"Neural Comput. Appl."}],"container-title":["Biomimetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-7673\/9\/6\/360\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:59:00Z","timestamp":1760108340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-7673\/9\/6\/360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,14]]},"references-count":40,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["biomimetics9060360"],"URL":"https:\/\/doi.org\/10.3390\/biomimetics9060360","relation":{},"ISSN":["2313-7673"],"issn-type":[{"value":"2313-7673","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,14]]}}}