Comparative Performance Analysis of Metaheuristic Feature Selection Methods for Speech Emotion Recognition

[1] Das, A., Guha, S., Singh, P. K., Ahmadian, A., Senu, N., Sarkar, R. (2020). A hybrid meta-heuristic feature selection method for identification of Indian spoken languages from audio signals. IEEE Access, 8, 181432-181449. https://doi.org/10.1109/ACCESS.2020.3028241 Search in Google Scholar

[2] Damasio, A. R. (2000). A second chance for emotion. In Cognitive Neuroscience of Emotion. Oxford University Press, 12-23. ISBN 9780195155921. Search in Google Scholar

[3] Ekman, P. (1992). Facial expressions of emotion: New findings, new questions. Psychological Science, 3 (1), 34-38. https://doi.org/10.1111/j.1467-9280.1992.tb00253.x Search in Google Scholar

[4] Ververidis, D., Kotropoulos, C. (2006). Emotional speech recognition: Resources, features, and methods. Speech Communication, 48 (9), 1162-1181. https://doi.org/10.1016/j.specom.2006.04.003 Search in Google Scholar

[5] Lee, C. M., Narayanan, S. S. (2005). Toward detecting emotions in spoken dialogs. IEEE Transactions on Speech and Audio Processing, 13 (2), 293-303. https://doi.org/10.1109/TSA.2004.838534 Search in Google Scholar

[6] Özseven, T. (2022). A review of infant cry recognition and classification based on computer-aided diagnoses. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE. https://doi.org/10.1109/HORA55278.2022.9800038 Search in Google Scholar

[7] Özseven, T. (2019). A novel feature selection method for speech emotion recognition. Applied Acoustics, 146, 320-326. https://doi.org/10.1016/j.apacoust.2018.11.028 Search in Google Scholar

[8] Bandela, S. R., Kumar, T. K. (2020). Speech emotion recognition using unsupervised feature selection algorithms. Radioengineering, 29 (2), 353-364. http://dx.doi.org/10.13164/re.2020.0353 Search in Google Scholar

[9] Pao, T.-L., Chen, Y.-T., Yeh, J.-H., Chang, Y.-H. (2005). Emotion recognition and evaluation of Mandarin speech using weighted D-KNN classification. In Proceedings of the 17th Conference on Computational Linguistics and Speech Processing. The Association for Computational Linguistics and Chinese Language Processing. Search in Google Scholar

[10] Ververidis, D., Kotropoulos, C. (2006). Fast sequential floating forward selection applied to emotional speech features estimated on DES and SUSAS data collections. In 2006 14th European Signal Processing Conference. IEEE. Search in Google Scholar

[11] Sidorova, J. (2009). Speech emotion recognition with TGI+.2 classifier. In Proceedings of the EACL 2009 Student Research Workshop. Association for Computational Linguistics (ACL), 54-60. Search in Google Scholar

[12] Haq, S., Jackson, P. J. B., Edge, J. D. (2008). Audio-visual feature selection and reduction for emotion classification. In Proceedings of International Conference on Auditory-Visual Speech Processing (AVSP 2008). AVISA, 185-190. ISBN 978-0-646-49504-0. Search in Google Scholar

[13] Kanwal, S., Asghar, S. (2021). Speech emotion recognition using clustering based GA-optimized feature set. IEEE Access, 9, 125830-125842. https://doi.org/10.1109/ACCESS.2021.3111659 Search in Google Scholar

[14] Tao, Y., Wang, K., Yang, J., An, N., Li, L. (2015). Harmony search for feature selection in speech emotion recognition. In 2015 International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 362-367. https://doi.org/10.1109/ACII.2015.7344596 Search in Google Scholar

[15] Liu, Z.-T., Wu, M., Cao, W.-H., Mao, J.-W., Xu, J.-P., Tan, G.-Z. (2018). Speech emotion recognition based on feature selection and extreme learning machine decision tree. Neurocomputing, 273, 271-280. https://doi.org/10.1016/j.neucom.2017.07.050 Search in Google Scholar

[16] Sun, L., Fu, S., Wang, F. (2019). Decision tree SVM model with Fisher feature selection for speech emotion recognition. EURASIP Journal on Audio, Speech, and Music Processing, 2019, 2. https://doi.org/10.1186/s13636-018-0145-5 Search in Google Scholar

[17] Yildirim, S., Kaya, Y., Kılıç, F. (2021). A modified feature selection method based on metaheuristic algorithms for speech emotion recognition. Applied Acoustics, 173, 107721. https://doi.org/10.1016/j.apacoust.2020.107721 Search in Google Scholar

[18] Panigrahi, S. N., Palo, H. K. (2021). Emotional speech recognition using particle swarm optimization algorithm. In 2021 International Conference in Advances in Power, Signal, and Information Technology (APSIT). IEEE. https://doi.org/10.1109/APSIT52773.2021.9641247 Search in Google Scholar

[19] Muthusamy, H., Polat, K., Yaacob, S. (2015). Particle swarm optimization based feature enhancement and feature selection for improved emotion recognition in speech and glottal signals. PLoS ONE, 10 (3), e0120344. https://doi.org/10.1371/journal.pone.0120344 Search in Google Scholar

[20] Yogesh, C. K., Hariharan, M., Ngadiran, R., Adom, A. H., Yaacob, S., Berkai, C., Polat, K. (2017). A new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signal. Expert Systems with Applications, 69, 149-158. https://doi.org/10.1016/j.eswa.2016.10.035 Search in Google Scholar

[21] Ding, N., Ye, N., Huang, H., Wang, R., Malekian, R. (2018). Speech emotion features selection based on BBO-SVM. In 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI). IEEE, 210-216. https://doi.org/10.1109/ICACI.2018.8377608 Search in Google Scholar

[22] Daneshfar, F., Kabudian, S. J., Neekabadi, A. (2020). Speech emotion recognition using hybrid spectral-prosodic features of speech signal/glottal waveform, metaheuristic-based dimensionality reduction, and Gaussian elliptical basis function network classifier. Applied Acoustics, 166, 107360. https://doi.org/10.1016/j.apacoust.2020.107360 Search in Google Scholar

[23] Bandela, S. R., Kumar, T. K. (2019). Speech emotion recognition using semi-NMF feature optimization. Turkish Journal of Electrical Engineering and Computer Sciences, 27 (5), 3741-3757. https://doi.org/10.3906/elk-1903-121 Search in Google Scholar

[24] Rajasekhar, B., Kamaraju, M., Sumalatha, V. (2020). A novel speech emotion recognition model using mean update of particle swarm and whale optimization-based deep belief network. Data Technologies and Applications, 54 (3), 297-322. https://doi.org/10.1108/DTA-07-2019-0120 Search in Google Scholar

[25] Dey, A., Chattopadhyay, S., Singh, P. K., Ahmadian, A., Ferrara, M., Sarkar, R. (2020). A hybrid meta-heuristic feature selection method using golden ratio and equilibrium optimization algorithms for speech emotion recognition. IEEE Access, 8, 200953-200970. https://doi.org/10.1109/ACCESS.2020.3035531 Search in Google Scholar

[26] Bagadi, K. R., Sivappagari, C. M. R. (2024). A robust feature selection method based on meta-heuristic optimization for speech emotion recognition. Evolutionary Intelligence, 17, 993-1004. https://doi.org/10.1007/s12065-022-00772-5 Search in Google Scholar

[27] Sun, L., Li, Q., Fu, S., Li, P. (2022). Speech emotion recognition based on genetic algorithm–decision tree fusion of deep and acoustic features. ETRI Journal, 44 (3), 462-475. https://doi.org/10.4218/etrij.2020-0458 Search in Google Scholar

[28] Gomathy, M. (2021). Optimal feature selection for speech emotion recognition using enhanced cat swarm optimization algorithm. International Journal of Speech Technology, 24 (1), 155-163. https://doi.org/10.1007/s10772-020-09776-x Search in Google Scholar

[29] Pan, L., Wang, S., Yin, Z., Song, A. (2022). Recognition of human inner emotion based on two-stage FCA-reliefF feature optimization. Information Technology and Control, 51 (1), 32-47. https://doi.org/10.5755/j01.itc.51.1.29430 Search in Google Scholar

[30] Chattopadhyay, S., Dey, A., Singh, P. K., Ahmadian, A., Sarkar, R. (2023). A feature selection model for speech emotion recognition using clustering-based population generation with hybrid of equilibrium optimizer and atom search optimization algorithm. Multimedia Tools and Applications, 82, 9693-9726. https://doi.org/10.1007/s11042-021-11839-3 Search in Google Scholar

[31] Kennedy, J., Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks. IEEE. https://doi.org/10.1109/ICNN.1995.488968 Search in Google Scholar

[32] Mirjalili, S., Mirjalili, S. M., Hatamlou, A. (2016). Multi-Verse Optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27 (2), 495-513. https://doi.org/10.1007/s00521-015-1870-7 Search in Google Scholar

[33] Mirjalili, S., Mirjalili, S. M., Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007 Search in Google Scholar

[34] Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228-249. https://doi.org/10.1016/j.knosys.2015.07.006 Search in Google Scholar

[35] Mirjalili, S., Lewis, A. (2016). The Whale Optimization Algorithm. Advances in Engineering Software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008 Search in Google Scholar

[36] Yang, X.-S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2 (2), 78-84. https://doi.org/10.1504/IJBIC.2010.032124 Search in Google Scholar

[37] Yang, X.-S. (2010). A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Springer, SCI 284, 65-74. https://doi.org/10.1007/978-3-642-12538-6_6 Search in Google Scholar

[38] Yang, X.-S., Deb, S. (2009). Cuckoo Search via Lévy flights. In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 210-214. https://doi.org/10.1109/NABIC.2009.5393690 Search in Google Scholar

[39] Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., Weiss, B. (2005). A database of German emotional speech. In INTERSPEECH 2005 - Eurospeech, 9th European Conference on Speech Communication and Technology. ISCA, 1517-1520. https://doi.org/10.21437/Interspeech.2005-446 Search in Google Scholar

[40] Martin, O., Kotsia, I., Macq, B., Pitas, I. (2006). The The eNTERFACE’ 05 audio-visual emotion database. In 22nd International Conference on Data Engineering Workshops (ICDEW’06). IEEE. https://doi.org/10.1109/ICDEW.2006.145 Search in Google Scholar

[41] Costantini, G., Iadarola, I., Paoloni, A., Todisco, M. (2014). EMOVO Corpus: an Italian emotional speech database. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14). ELRA, 3501-3504. Search in Google Scholar

[42] Rabiner, L. R. (1968). Digital-formant synthesizer for speech-synthesis studies. The Journal of the Acoustical Society of America, 43 (4), 822-828. https://doi.org/10.1121/1.1910901 Search in Google Scholar

[43] Eyben, F., Weninger, F., Gross, F., Schuller, B. (2013). Recent developments in openSMILE, the munich open-source multimedia feature extractor. In MM ‘13: Proceedings of the 21st ACM International Conference on Multimedia. ACM, 835-838. https://doi.org/10.1145/2502081.2502224 Search in Google Scholar

[44] Özseven, T., Düğenci, M. (2018). SPeech ACoustic (SPAC): A novel tool for speech feature extraction and classification. Applied Acoustics, 136, 1-8. Search in Google Scholar

[45] Song, P., Zheng, W., Yu, Y., Ou, S. (2021). Speech emotion recognition based on robust discriminative sparse regression. IEEE Transactions on Cognitive and Developmental Systems, 13 (2), 343-353. https://doi.org/10.1109/TCDS.2020.2990928 Search in Google Scholar

[46] Khurma, R. A., Aljarah, I., Sharieh, A., Mirjalili, S. (2020). EvoloPy-FS: An open-source nature-inspired optimization framework in Python for feature selection. In Evolutionary Machine Learning Techniques: Algorithms and Applications. Spinger, 131-173. https://doi.org/10.1007/978-981-32-9990-0_8 Search in Google Scholar

[47] Guangyou, Y. (2007). A modified particle swarm optimizer algorithm. In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE. https://doi.org/10.1109/ICEMI.2007.4350772 Search in Google Scholar

[48] Yılmaz, Ö., Altun, A. A., Köklü, M. (2022). Optimizing the learning process of multi-layer perceptrons using a hybrid algorithm based on MVO and SA. International Journal of Industrial Engineering Computations, 13 (4), 617-640. https://doi.org/10.5267/j.ijiec.2022.5.003 Search in Google Scholar

[49] Ma, C., Huang, H., Fan, Q., Wei, J., Du, Y., Gao, W. (2022). Grey wolf optimizer based on Aquila exploration method. Expert Systems with Applications, 205, 117629. https://doi.org/10.1016/j.eswa.2022.117629 Search in Google Scholar

[50] Nadimi-Shahraki, M. H., Banaie-Dezfouli, M., Zamani, H., Taghian, S., Mirjalili, S. (2021). B-MFO: A binary moth-flame optimization for feature selection from medical datasets. Computers, 10 (11), 136. https://doi.org/10.3390/computers10110136 Search in Google Scholar

[51] Sharawi, M., Zawbaa, H. M., Emary, E. (2017). Feature selection approach based on whale optimization algorithm. In 2017 Ninth International Conference on Advanced Computational Intelligence (ICACI). IEEE, 163-168. https://doi.org/10.1109/ICACI.2017.7974502 Search in Google Scholar

[52] Xu, H., Yu, S., Chen, J., Zuo, X. (2018). An improved firefly algorithm for feature selection in classification. Wireless Personal Communications, 102 (4), 2823-2834. https://doi.org/10.1007/s11277-018-5309-1 Search in Google Scholar

[53] Nakamura, R. Y. M., Pereira, L. A. M., Costa, K. A., Rodrigues, D., Papa, J. P., Yang, X.-S. (2012). BBA: A binary bat algorithm for feature selection. In 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images. IEEE. https://doi.org/10.1109/SIBGRAPI.2012.47 Search in Google Scholar

[54] Huang, S., Dang, H., Jiang, R., Hao, Y., Xue, C., Gu, W. (2021). Multi-layer hybrid fuzzy classification based on SVM and improved PSO for speech emotion recognition. Electronics, 10 (23), 2891. https://doi.org/10.3390/electronics10232891 Search in Google Scholar

[55] Wang, L. (ed.) (2005). Support Vector Machines: Theory and Applications. Springer, STUDFUZZ 177. https://doi.org/10.1007/b95439 Search in Google Scholar

[56] Al Dujaili, M. J., Ebrahimi-Moghadam, A., Fatlawi, A. (2021). Speech emotion recognition based on SVM and KNN classifications fusion. International Journal of Electrical and Computer Engineering (IJECE), 11 (2), 1259. http://doi.org/10.11591/ijece.v11i2.pp1259-1264 Search in Google Scholar

[57] Challita, N., Khalil, M., Beauseroy, P. (2016). New feature selection method based on neural network and machine learning. In 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET). IEEE, 81-85. https://doi.org/10.1109/IMCET.2016.7777431 Search in Google Scholar

[58] Albadr, M. A. A., Tiun, S., Ayob, M., AL-Dhief, F. T., Omar, K., Maen, M. K. (2022). Speech emotion recognition using optimized genetic algorithm-extreme learning machine. Multimedia Tools and Applications, 81 (17), 23963-23989. https://doi.org/10.1007/s11042-022-12747-w Search in Google Scholar

[59] Li, C.-Z., Liu, F.-K., Wang, Y.-T., Wang, H., Zhang, Q. (2017). Speech emotion recognition based on PSO-optimized SVM. In 2nd International Conference on Software, Multimedia and Communication Engineering (SMCE 2017). DEStech Publications. https://doi.org/10.12783/dtcse/smce2017/12465 Search in Google Scholar

[60] Zhang, Z. (2021). Speech feature selection and emotion recognition based on weighted binary cuckoo search. Alexandria Engineering Journal, 60 (1), 1499-1507. https://doi.org/10.1016/j.aej.2020.11.004 Search in Google Scholar