{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T09:15:54Z","timestamp":1775034954105,"version":"3.50.1"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031232350","type":"print"},{"value":"9783031232367","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-23236-7_27","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T01:22:43Z","timestamp":1672536163000},"page":"389-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["F0, LPC, and\u00a0MFCC Analysis for\u00a0Emotion Recognition Based on\u00a0Speech"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3439-826X","authenticated-orcid":false,"given":"Felipe L.","family":"Teixeira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6679-5702","authenticated-orcid":false,"given":"Jo\u00e3o Paulo","family":"Teixeira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5862-5706","authenticated-orcid":false,"given":"Salviano F. P.","family":"Soares","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9414-1143","authenticated-orcid":false,"given":"J. L. Pio","family":"Abreu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2019.102447","volume":"149","author":"NJ Shoumy","year":"2020","unstructured":"Shoumy, N.J., Ang, L.M., Seng, K.P., Rahaman, D.M., Zia, T.: Multimodal big data affective analytics: a comprehensive survey using text, audio, visual and physiological signals. J. Network Comput. Appl. 149, 102447 (2020)","journal-title":"J. Network Comput. Appl."},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Lalitha, S., Madhavan, A., Bhushan, B., Saketh, S.: Speech emotion recognition. In: 2014 International Conference on Advances in Electronics, Computers and Communications, vol. 7, pp. 7\u201310 (2015)","DOI":"10.1109\/ICAECC.2014.7002390"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Shah Fahad, M., Ranjan, A., Yadav, J., Deepak, A.: A survey of speech emotion recognition in natural environment. Digital Sig. Process. Rev. J. 110, 102951 (2021)","DOI":"10.1016\/j.dsp.2020.102951"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Bandela, S.R., Kumar, T.K.: Speech emotion recognition using unsupervised feature selection algorithms. Radioengineering 29, 353\u2013364 (2020)","DOI":"10.13164\/re.2020.0353"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Imani, M., Montazer, G.A.: A survey of emotion recognition methods with emphasis on E-Learning environments. J. Network Comput. Appl. 147, 102423 (2019)","DOI":"10.1016\/j.jnca.2019.102423"},{"key":"27_CR6","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1631\/jzus.CIDE1310","volume":"14","author":"QR Mao","year":"2013","unstructured":"Mao, Q.R., Zhao, X.L., Huang, Z.W., Zhan, Y.Z.: Speaker-independent speech emotion recognition by fusion of functional and accompanying paralanguage features. J. Zhejiang Univ. Sci. C 14, 573\u2013582 (2013)","journal-title":"J. Zhejiang Univ. Sci. C"},{"issue":"3","key":"27_CR7","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/j.patcog.2010.09.020","volume":"44","author":"M El Ayadi","year":"2011","unstructured":"El Ayadi, M., Kamel, M.S., Karray, F.: Survey on speech emotion recognition: features, classification schemes, and databases. Pattern Recogn. 44(3), 572\u2013587 (2011)","journal-title":"Pattern Recogn."},{"key":"27_CR8","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1016\/j.procs.2021.06.065","volume":"190","author":"IS Malova","year":"2021","unstructured":"Malova, I.S., Tikhomirova, D.V.: Recognition of emotions in verbal messages based on neural networks. Procedia Comput. Sci. 190, 560\u2013563 (2021)","journal-title":"Procedia Comput. Sci."},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s40009-020-00907-1","volume":"43","author":"P Vasuki","year":"2020","unstructured":"Vasuki, P., Sambavi, B., Joe, V.: Construction and evaluation of tamil speech emotion corpus. Natl. Acad. Sci. Let. 43, 533\u2013536 (2020)","journal-title":"Natl. Acad. Sci. Let."},{"issue":"3","key":"27_CR10","doi-asserted-by":"publisher","first-page":"1467","DOI":"10.1007\/s11235-011-9624-z","volume":"52","author":"S Ramakrishnan","year":"2013","unstructured":"Ramakrishnan, S., El Emary, I.M.: Speech emotion recognition approaches in human computer interaction. Telecommun. Syst. 52(3), 1467\u20131478 (2013)","journal-title":"Telecommun. Syst."},{"key":"27_CR11","doi-asserted-by":"publisher","unstructured":"Nunes, A., Coimbra, R.L., Teixeira, A.: Voice quality of european portuguese emotional speech. In: Pardo, T.A.S., Branco, A., Klautau, A., Vieira, R., de Lima, V.S. (eds.) PROPOR 2010. LNCS (LNAI), vol. 6001, pp. 142\u2013151. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12320-7_19","DOI":"10.1007\/978-3-642-12320-7_19"},{"key":"27_CR12","unstructured":"Lopes, R.P., et al.: Digital Technologies for Innovative Mental Health Rehabilitation, pp. 1\u201315 (2021)"},{"key":"27_CR13","doi-asserted-by":"publisher","first-page":"117327","DOI":"10.1109\/ACCESS.2019.2936124","volume":"7","author":"RA Khalil","year":"2019","unstructured":"Khalil, R.A., Jones, E., Babar, M.I., Jan, T., Zafar, M.H., Alhussain, T.: Speech emotion recognition using deep learning techniques: a review. IEEE Access 7, 117327\u2013117345 (2019)","journal-title":"IEEE Access"},{"key":"27_CR14","doi-asserted-by":"crossref","unstructured":"Rodrigues, P.M., Teixeira, J.P.: Alzheimer\u2019s disease recognition with artificial neural networks, pp. 112\u2013119 (2013)","DOI":"10.4018\/978-1-4666-3667-5.ch007"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Rodrigues, P.M., Teixeira, J.P.: Classification of electroencephalogram signals using artificial neural networks. In: Proceedings - 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010, vol. 2, pp. 808\u2013812 (2010)","DOI":"10.1109\/BMEI.2010.5639941"},{"key":"27_CR16","doi-asserted-by":"publisher","unstructured":"Rodrigues, P., Teixeira, J.: Artificial neural networks in the discrimination of alzheimer\u2019s disease. In: Cruz-Cunha, M.M., Varaj\u00e3o, J., Powell, P., Martinho, R. (eds.) CENTERIS 2011. CCIS, vol. 221, pp. 272\u2013281. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-24352-3_29","DOI":"10.1007\/978-3-642-24352-3_29"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Guedes, V., Junior, A., Fernandes, J., Teixeira, F., Teixeira, J.: Long short term memory on chronic laryngitis classification. In: Procedia Computer Science, vol. 138 (2018)","DOI":"10.1016\/j.procs.2018.10.036"},{"key":"27_CR18","doi-asserted-by":"crossref","unstructured":"Guedes, V., et al.: Transfer learning with audioset to voice pathologies identification in continuous speech. Procedia Comput. Sci. 164, 662\u2013669 (2019)","DOI":"10.1016\/j.procs.2019.12.233"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Panahi, F., Rashidi, S., Sheikhani, A.: Application of fractional Fourier transform in feature extraction from ELECTROCARDIOGRAM and GALVANIC SKIN RESPONSE for emotion recognition. Biomed. Signal Process. Control 69, 102863 (2021)","DOI":"10.1016\/j.bspc.2021.102863"},{"key":"27_CR20","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.psychres.2018.08.040","volume":"269","author":"A Pestana-Santos","year":"2018","unstructured":"Pestana-Santos, A., Loureiro, L., Santos, V., Carvalho, I.: Patients with schizophrenia assessing psychiatrists\u2019 communication skills. Psych. Res. 269, 13\u201320 (2018)","journal-title":"Psych. Res."},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Ververidis, D., Kotropoulos, C.: Emotional speech recognition: resources, features, and methods. Speech Commun. 48, 1162\u20131181 (2006)","DOI":"10.1016\/j.specom.2006.04.003"},{"key":"27_CR22","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.specom.2019.12.001","volume":"116","author":"MB Ak\u00e7ay","year":"2020","unstructured":"Ak\u00e7ay, M.B., O\u011fuz, K.: Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 116, 56\u201376 (2020)","journal-title":"Speech Commun."},{"issue":"8","key":"27_CR23","doi-asserted-by":"publisher","first-page":"83","DOI":"10.5755\/j01.eee.19.8.1739","volume":"19","author":"J Pribil","year":"2013","unstructured":"Pribil, J., Pribilova, A., Matousek, J.: Comparison of formant features of male and female emotional speech in czech and slovak. Elektronika ir Elektrotechnika 19(8), 83\u201388 (2013)","journal-title":"Elektronika ir Elektrotechnika"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Prasanth, S., Roshni Thanka, M., Bijolin Edwin, E., Nagaraj, V.: Speech emotion recognition based on machine learning tactics and algorithms. Mater. Today Proc. (2021)","DOI":"10.1016\/j.matpr.2020.12.207"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Rao, K.S., Koolagudi, S.G., Vempada, R.R.: Emotion recognition from speech using global and local prosodic features. Int. J. Speech Technol. 16, 143\u2013160 (2013)","DOI":"10.1007\/s10772-012-9172-2"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Shen, P., Changjun, Z., Chen, X.: Automatic speech emotion recognition using support vector machine. In: Proceedings of 2011 International Conference on Electronic and Mechanical Engineering and Information Technology, EMEIT 2011, vol. 2, pp. 621\u2013625 (2011)","DOI":"10.1109\/EMEIT.2011.6023178"},{"key":"27_CR27","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1109\/TBME.2010.2091640","volume":"58","author":"LSA Low","year":"2011","unstructured":"Low, L.S.A., Maddage, N.C., Lech, M., Sheeber, L.B., Allen, N.B.: Detection of clinical depression in adolescents\u2019 speech during family interactions. IEEE Trans. Biomed. Eng. 58, 574\u2013586 (2011)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"7","key":"27_CR28","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.specom.2010.02.010","volume":"52","author":"D Bitouk","year":"2010","unstructured":"Bitouk, D., Verma, R., Nenkova, A.: Class-level spectral features for emotion recognition. Speech Commun. 52(7), 613\u2013625 (2010)","journal-title":"Speech Commun."},{"key":"27_CR29","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.procs.2016.08.239","volume":"96","author":"R Chakraborty","year":"2016","unstructured":"Chakraborty, R., Pandharipande, M., Kopparapu, S.K.: Knowledge-based framework for intelligent emotion recognition in spontaneous speech. Procedia Comput. Sci. 96, 587\u2013596 (2016)","journal-title":"Procedia Comput. Sci."},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Abdulmohsin, H.A. Abdul Wahab, H.B., Abdul Hossen, A.M.J.: A new proposed statistical feature extraction method in speech emotion recognition. Comput. Electric. Eng. 93, 107172 (2021)","DOI":"10.1016\/j.compeleceng.2021.107172"},{"issue":"2","key":"27_CR31","first-page":"384","volume":"34","author":"K Mannepalli","year":"2022","unstructured":"Mannepalli, K., Sastry, P.N., Suman, M.: Emotion recognition in speech signals using optimization based multi-SVNN classifier. J. King Saud Univ. Comput. Inform. Sci. 34(2), 384\u2013397 (2022)","journal-title":"J. King Saud Univ. Comput. Inform. Sci."},{"key":"27_CR32","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1016\/j.procs.2021.01.251","volume":"181","author":"L Silva","year":"2021","unstructured":"Silva, L., Bispo, B., Teixeira, J.P.: Features selection algorithms for classification of voice signals. Procedia Comput. Sci. 181, 948\u2013956 (2021)","journal-title":"Procedia Comput. Sci."},{"key":"27_CR33","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.procs.2018.10.040","volume":"138","author":"J Fernandes","year":"2018","unstructured":"Fernandes, J., Teixeira, F., Guedes, V., Junior, A., Teixeira, J.P.: Harmonic to noise ratio measurement - selection of window and length. Procedia Comput. Sci. 138, 280\u2013285 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"27_CR34","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.procs.2016.09.155","volume":"100","author":"JP Teixeira","year":"2016","unstructured":"Teixeira, J.P., Gon\u00e7alves, A.: Algorithm for jitter and shimmer measurement in pathologic voices. Procedia Comput. Sci. 100, 271\u2013279 (2016)","journal-title":"Procedia Comput. Sci."},{"key":"27_CR35","unstructured":"Bohadana, S.C.: Vibra\u00e7\u00e3o das pregas vocais pr\u00e9 e p\u00f3s aproxima\u00e7\u00e3o cricotire\u00f3idea: estudo experimental em laringes humanas por videoquimografia (2001)"},{"issue":"1","key":"27_CR36","doi-asserted-by":"publisher","first-page":"51","DOI":"10.18280\/ts.370107","volume":"37","author":"S Demircan","year":"2020","unstructured":"Demircan, S., \u00d6rnek, H.K.: Comparison of the effects of mel coefficients and spectrogram images via deep learning in emotion classification. Traitement du Signal 37(1), 51\u201357 (2020)","journal-title":"Traitement du Signal"},{"key":"27_CR37","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.ymssp.2016.11.017","volume":"88","author":"B Dong","year":"2017","unstructured":"Dong, B.: Characterizing resonant component in speech: a different view of tracking fundamental frequency. Mech. Syst. Sign. Process. 88, 318\u2013333 (2017)","journal-title":"Mech. Syst. Sign. Process."},{"key":"27_CR38","unstructured":"Netto, W.F., Traunmuller, H., Eriksson, A.: A extens\u00e3o da frequ\u00eancia fundamental da voz na fala de homens e mulheres adultos (2009)"},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Struwe, K.: Voiced-unvoiced classification of speech using a neural network trained with LPC coefficients. In: Proceedings - 2017 International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2017, pp. 56\u201359 (2017)","DOI":"10.1109\/ICCAIRO.2017.20"},{"key":"27_CR40","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.procs.2019.12.232","volume":"164","author":"J Fernandes","year":"2019","unstructured":"Fernandes, J., Silva, L., Teixeira, F., Guedes, V., Santos, J., Teixeira, J.P.: Parameters for vocal acoustic analysis - cured database. Procedia Comput. Sci. 164, 654\u2013661 (2019)","journal-title":"Procedia Comput. Sci."},{"key":"27_CR41","unstructured":"Oliveira, L.C., Paulo, S., Figueira, L., Mendes, C., Nunes, A., Godinho, J.: Methodologies for designing and recording speech databases for corpus based synthesis,\" Proceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2008, pp. 2921\u20132925 (2008)"},{"key":"27_CR42","unstructured":"Saratxaga, I., Navas, E., Hern\u00e1ez, I., Luengo, I.: Designing and recording an emotional speech database for corpus based synthesis in Basque. Proceedings of the 5th International Conference on Language Resources and Evaluation, LREC 2006, pp. 2126\u20132129 (2006)"},{"key":"27_CR43","doi-asserted-by":"crossref","unstructured":"Teixeira, F.L., Teixeira, J.P.: Deep-learning in identification of vocal pathologies. In: BIOSIGNALS 2020\u201313th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, vol. 4, no. Biostec 2020, pp. 288\u2013295 (2020)","DOI":"10.5220\/0009148802880295"}],"container-title":["Communications in Computer and Information Science","Optimization, Learning Algorithms and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-23236-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T02:30:45Z","timestamp":1672540245000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-23236-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031232350","9783031232367"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-23236-7_27","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"OL2A","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Optimization, Learning Algorithms and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bragan\u00e7a","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ol2a2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ol2a.ipb.pt\/EN_index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"145","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}