{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T04:35:31Z","timestamp":1772080531206,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works.<\/jats:p>","DOI":"10.3390\/s22166206","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T23:28:41Z","timestamp":1660865321000},"page":"6206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception"],"prefix":"10.3390","volume":"22","author":[{"given":"Effat","family":"Jalaeian Zaferani","sequence":"first","affiliation":[{"name":"Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"given":"Mohammad","family":"Teshnehlab","sequence":"additional","affiliation":[{"name":"Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2374-0557","authenticated-orcid":false,"given":"Amirreza","family":"Khodadadian","sequence":"additional","affiliation":[{"name":"Institute of Applied Mathematics, Leibniz University of Hannover, 30167 Hannover, Germany"}]},{"given":"Clemens","family":"Heitzinger","sequence":"additional","affiliation":[{"name":"Institute of Analysis and Scientific Computing, TU Wien, 1040 Vienna, Austria"},{"name":"Center for Artificial Intelligence and Machine Learning (CAIML), TU Wien, 1040 Vienna, Austria"}]},{"given":"Mansour","family":"Vali","sequence":"additional","affiliation":[{"name":"Electrical & Computer Engineering Faculty, K. N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"given":"Nima","family":"Noii","sequence":"additional","affiliation":[{"name":"Institute of Continuum Mechanics, Leibniz University of Hannover, 30823 Garbsen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1102-6332","authenticated-orcid":false,"given":"Thomas","family":"Wick","sequence":"additional","affiliation":[{"name":"Institute of Applied Mathematics, Leibniz University of Hannover, 30167 Hannover, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"key":"ref_1","unstructured":"Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., and Boulesteix, A.-L. (2021). Hyperparameter optimization: Foundations, algorithms, best practices and open challenges. arXiv."},{"key":"ref_2","first-page":"1","article-title":"Explaining Artificial Intelligence with Care","volume":"16","author":"Szepannek","year":"2022","journal-title":"KI-K\u00fcnstliche Intell."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Khodadadian, A., Parvizi, M., Teshnehlab, M., and Heitzinger, C. (2022). Rational Design of Field-Effect Sensors Using Partial Differential Equations, Bayesian Inversion, and Artificial Neural Networks. Sensors, 22.","DOI":"10.3390\/s22134785"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"131248","DOI":"10.1109\/ACCESS.2020.3009644","article-title":"A Deep Learning Based Fault Diagnosis Method With hyperparameter Optimization by Using Parallel Computing","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Feurer, M., and Hutter, F. (2019). Hyperparameter optimization. Automated Machine Learning, Springer.","DOI":"10.1007\/978-3-030-05318-5_1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, D., and Wu, C. (2022). Research on the Time-Dependent Split Delivery Green Vehicle Routing Problem for Fresh Agricultural Products with Multiple Time Windows. Agriculture, 12.","DOI":"10.3390\/agriculture12060793"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2703","DOI":"10.1007\/s10489-021-02507-y","article-title":"An automatic hyperparameter optimization DNN model for precipitation prediction","volume":"52","author":"Peng","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5486","DOI":"10.1109\/TITS.2020.2987614","article-title":"An automated hyperparameter search-based deep learning model for highway traffic prediction","volume":"22","author":"Yi","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","unstructured":"Kinnewig, S., Kolditz, L., Roth, J., and Wick, T. (2022). Numerical Methods for Algorithmic Systems and Neural Networks, Institut f\u00fcr Angewandte Mathematik, Leibniz Universit\u00e4t Hannover."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1007\/s42835-020-00343-7","article-title":"Hyperparameter optimization using a genetic algorithm considering verification time in a convolutional neural network","volume":"15","author":"Han","year":"2020","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.isatra.2021.07.017","article-title":"A novel mathematical morphology spectrum entropy based on scale-adaptive techniques","volume":"126","author":"Yao","year":"2022","journal-title":"ISA Trans."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Raji, I.D., Bello-Salau, H., Umoh, I.J., Onumanyi, A.J., Adegboye, M.A., and Salawudeen, A.T. (2022). Simple deterministic selection-based genetic algorithm for hyperparameter tuning of machine learning models. Appl. Sci., 12.","DOI":"10.3390\/app12031186"},{"key":"ref_14","unstructured":"Harichandana, B., and Kumar, S. (2022, January 26\u201328). LEAPMood: Light and Efficient Architecture to Predict Mood with Genetic Algorithm driven Hyperparameter Tuning. Proceedings of the 2022 IEEE 16th International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guido, R., Groccia, M.C., and Conforti, D. (2022). Hyper-Parameter Optimization in Support Vector Machine on Unbalanced Datasets Using Genetic Algorithms. Optimization in Artificial Intelligence and Data Sciences, Springer.","DOI":"10.1007\/978-3-030-95380-5_4"},{"key":"ref_16","first-page":"4031","article-title":"Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things-23 dataset","volume":"12","author":"Thavasimani","year":"2022","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_17","first-page":"8278","article-title":"Optimizing the Topology and Learning Parameters of Hierarchical RBF Neural Networks Using Genetic Algorithms","volume":"13","author":"Awad","year":"2018","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1007\/s13042-018-00913-2","article-title":"Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme","volume":"10","author":"Faris","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, A., Spyra, O., Perel, S., Dalibard, V., Jaderberg, M., Gu, C., Budden, D., Harley, T., and Gupta, P. (2019, January 4\u20138). A generalized framework for population based training. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330649"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s13721-016-0125-6","article-title":"A review of automatic selection methods for machine learning algorithms and hyper-parameter values","volume":"5","author":"Luo","year":"2016","journal-title":"Netw. Model. Anal. Health Inform. Bioinform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"An, Z., Wang, X., Li, B., Xiang, Z., and Zhang, B. (2022). Robust visual tracking for UAVs with dynamic feature weight selection. Appl. Intell.","DOI":"10.1007\/s10489-022-03719-6"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"26","DOI":"10.21307\/ijanmc-2021-024","article-title":"Designing convolutional neural network architecture using genetic algorithms","volume":"6","author":"Bhandare","year":"2021","journal-title":"Int. J. Adv. Netw. Monit. Control"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tan, H.H., and Lim, K.H. (2019, January 28\u201330). Vanishing gradient mitigation with deep learning neural network optimization. Proceedings of the 2019 7th International Conference on Smart Computing & Communications (ICSCC), Sarawak, Malaysia.","DOI":"10.1109\/ICSCC.2019.8843652"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"68595","DOI":"10.1109\/ACCESS.2021.3076820","article-title":"Automatic Personality Traits Perception Using Asymmetric Auto-Encoder","volume":"9","author":"Zaferani","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"52588","DOI":"10.1109\/ACCESS.2020.2981072","article-title":"Basic enhancement strategies when using bayesian optimization for hyperparameter tuning of deep neural networks","volume":"8","author":"Cho","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3840","DOI":"10.1109\/TCYB.2020.2983860","article-title":"Automatically designing CNN architectures using the genetic algorithm for image classification","volume":"50","author":"Sun","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7593","DOI":"10.1007\/s00500-019-04387-4","article-title":"Hyperparameter optimization in CNN for learning-centered emotion recognition for intelligent tutoring systems","volume":"24","author":"Cabada","year":"2020","journal-title":"Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7319","DOI":"10.1109\/TIM.2020.2983233","article-title":"An improved quantum-inspired differential evolution algorithm for deep belief network","volume":"69","author":"Deng","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1080\/01969722.2020.1827797","article-title":"Efficient hyperparameter optimization for convolution neural networks in deep learning: A distributed particle swarm optimization approach","volume":"52","author":"Guo","year":"2020","journal-title":"Cybern. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"26587","DOI":"10.1007\/s11042-020-09268-9","article-title":"Static facial expression recognition using convolutional neural networks based on transfer learning and hyperparameter optimization","volume":"79","author":"Ozcan","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"52528","DOI":"10.1109\/ACCESS.2020.2981141","article-title":"Hyper-parameter selection in convolutional neural networks using microcanonical optimization algorithm","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6081","DOI":"10.1002\/er.7548","article-title":"State-of-health estimation and remaining useful life for lithium-ion battery based on deep learning with Bayesian hyperparameter optimization","volume":"46","author":"Kong","year":"2022","journal-title":"Int. J. Energy Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6407","DOI":"10.1109\/ACCESS.2022.3141781","article-title":"Deepqgho: Quantized greedy hyperparameter optimization in deep neural networks for on-the-fly learning","volume":"10","author":"Chowdhury","year":"2022","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2781","DOI":"10.1109\/JSTARS.2021.3059451","article-title":"A hyperspectral image classification method using multifeature vectors and optimized KELM","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e12624","DOI":"10.1111\/spc3.12624","article-title":"Personality computing: New frontiers in personality assessment","volume":"15","author":"Phan","year":"2021","journal-title":"Soc. Personal. Psychol. Compass"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Koutsombogera, M., Sarthy, P., and Vogel, C. (2020, January 7\u20139). Acoustic Features in Dialogue Dominate Accurate Personality Trait Classification. Proceedings of the 2020 IEEE International Conference on Human-Machine Systems (ICHMS), Rome, Italy.","DOI":"10.1109\/ICHMS49158.2020.9209445"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104163","DOI":"10.1016\/j.imavis.2021.104163","article-title":"Multimodal assessment of apparent personality using feature attention and error consistency constraint","volume":"110","author":"Aslan","year":"2021","journal-title":"Image Vis. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"76822","DOI":"10.1109\/ACCESS.2021.3076989","article-title":"Prediction of the Big Five Personality Traits Using Static Facial Images of College Students With Different Academic Backgrounds","volume":"9","author":"Xu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kampman, O., Siddique, F.B., Yang, Y., and Fung, P. (2019). Adapting a virtual agent to user personality. Advanced Social Interaction with Agents, Springer.","DOI":"10.1007\/978-3-319-92108-2_13"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13673-020-0208-3","article-title":"Intelligent video interview agent used to predict communication skill and perceived personality traits","volume":"10","author":"Suen","year":"2020","journal-title":"Hum.-Cent. Comput. Inf. Sci."},{"key":"ref_41","unstructured":"Liam Kinney, A.W., and Zhao, J. (2017). Detecting Personality Traits in Conversational Speech. Stanford University. Available online: https:\/\/web.stanford.edu\/class\/cs224s\/project\/reports_2017\/Liam_Kinney.pdf."},{"key":"ref_42","first-page":"197","article-title":"Automatic personality recognition and perception using deep learning and supervised evaluation method","volume":"9","author":"Teshnehlab","year":"2022","journal-title":"J. Appl. Res. Ind. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mohammadi, G., Vinciarelli, A., and Mortillaro, M. (2010, January 29). The voice of personality: Mapping nonverbal vocal behavior into trait attributions. Proceedings of the 2nd international workshop on Social signal processing, Firenze, Italy.","DOI":"10.1145\/1878116.1878123"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rosenberg, A. (2018, January 13\u201316). Speech, Prosody, and Machines: Nine Challenges for Prosody Research. Proceedings of the 9th International Conference on Speech Prosody 2018, Pozna\u0144, Poland.","DOI":"10.21437\/SpeechProsody.2018-159"},{"key":"ref_45","unstructured":"Junior, J.C.S.J., G\u00fc\u00e7l\u00fct\u00fcrk, Y., P\u00e9rez, M., G\u00fc\u00e7l\u00fc, U., Andujar, C., Bar\u00f3, X., Escalante, H., Guyon, I., van Gerven, M., and van Lier, R. (2019). First impressions: A survey on computer vision-based apparent personality trait analysis. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.csl.2018.02.004","article-title":"Affective and behavioural computing: Lessons learnt from the first computational paralinguistics challenge","volume":"53","author":"Schuller","year":"2019","journal-title":"Comput. Speech Lang."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hutter, F., Kotthoff, L., and Vanschoren, J. (2019). Automated Machine Learning: Methods, Systems, Challenges, Springer.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400031","article-title":"Parallel genetic algorithms: A useful survey","volume":"53","author":"Harada","year":"2020","journal-title":"ACM Comput. Surv."},{"key":"ref_49","first-page":"357","article-title":"A novel multi-population passing vehicle search algorithm based co-evolutionary cultural algorithm","volume":"16","author":"Chentoufi","year":"2021","journal-title":"Comput. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"368504211023277","DOI":"10.1177\/00368504211023277","article-title":"Optimization design of curved outrigger structure based on buckling analysis and multi-island genetic algorithm","volume":"104","author":"Liu","year":"2021","journal-title":"Sci. Prog."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Shah, P., and Kobti, Z. (2020, January 19\u201324). Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.","DOI":"10.1109\/CEC48606.2020.9185643"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"5280","DOI":"10.1007\/s11227-019-02776-y","article-title":"Island flower pollination algorithm for global optimization","volume":"75","author":"Awadallah","year":"2019","journal-title":"J. Supercomput."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.neucom.2011.08.043","article-title":"A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems","volume":"98","author":"Sun","year":"2012","journal-title":"Neurocomputing"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"da Silva, D.J.A., Teixeira, O.N., and de Oliveira, R.C.L. (2012). Performance Study of Cultural Algorithms Based on Genetic Algorithm with Single and Multi Population for the MKP. Bio-Inspired Computational Algorithms and Their Applitions, IntechOpen.","DOI":"10.5772\/36366"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhao, X., Tang, Z., Cao, F., Zhu, C., and Periaux, J. (2022). An Efficient Hybrid Evolutionary Optimization Method Coupling Cultural Algorithm with Genetic Algorithms and Its Application to Aerodynamic Shape Design. Appl. Sci., 12.","DOI":"10.3390\/app12073482"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"012152","DOI":"10.1088\/1742-6596\/1441\/1\/012152","article-title":"Fuzzy cultural algorithm for solving optimization problems","volume":"1441","author":"Muhamediyeva","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1007\/s00521-011-0749-5","article-title":"A multi-population cultural algorithm with adaptive diversity preservation and its application in ammonia synthesis process","volume":"21","author":"Xu","year":"2012","journal-title":"Neural Comput. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Cho, K.H., Raiko, T., and Ilin, A. (2013, January 4\u20139). Gaussian-bernoulli deep boltzmann machine. Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6706831"},{"key":"ref_60","first-page":"87","article-title":"A gaussian-gaussian-restricted-boltzmann-machine-based deep neural network technique for photovoltaic system generation forecasting","volume":"52","author":"Ogawa","year":"2019","journal-title":"IFAC-Pap."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"169","DOI":"10.3233\/AIC-170729","article-title":"Linear discriminant analysis: A detailed tutorial","volume":"30","author":"Tharwat","year":"2017","journal-title":"AI Commun."},{"key":"ref_62","first-page":"150","article-title":"A literature survey of benchmark functions for global optimisation problems","volume":"4","author":"Jamil","year":"2013","journal-title":"Int. J. Math. Model. Numer. Optim."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1109\/T-AFFC.2012.5","article-title":"Automatic personality perception: Prediction of trait attribution Based Prosodic Features","volume":"3","author":"Mohammadi","year":"2012","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_64","first-page":"16","article-title":"Personality traits detection using a parallelized modified SFFS algorithm","volume":"15","author":"Chastagnol","year":"2012","journal-title":"Computing"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Mohammadi, G., and Vinciarelli, A. (2015, January 21\u201324). Automatic personality perception: Prediction of trait attribution based on prosodic features extended abstract. Proceedings of the 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), Xi\u2019an, China.","DOI":"10.1109\/ACII.2015.7344614"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Solera-Ure\u00f1a, R., Moniz, H., Batista, F., Cabarr\u00e3o, R., Pompili, A., Astudillo, R., Campos, J., Paiva, A., and Trancoso, I. (2017, January 20\u201324). A semi-supervised learning approach for acoustic-prosodic personality perception in under-resourced domains. Proceedings of the 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017, Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-1732"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/TAFFC.2017.2763132","article-title":"Feature learning from spectrograms for assessment of personality traits","volume":"11","author":"Carbonneau","year":"2017","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.1109\/TMM.2020.3025108","article-title":"Speech personality recognition based on annotation classification using log-likelihood distance and extraction of essential audio features","volume":"23","author":"Liu","year":"2020","journal-title":"IEEE Trans. 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