
{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:49:49Z","timestamp":1778287789025,"version":"3.51.4"},"reference-count":105,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Council for Scientific and Technological Development (CNPq)","award":["303854\/222-7"],"award-info":[{"award-number":["303854\/222-7"]}]},{"name":"National Council for Scientific and Technological Development (CNPq)","award":["2021\/12407-4"],"award-info":[{"award-number":["2021\/12407-4"]}]},{"name":"National Council for Scientific and Technological Development (CNPq)","award":["2022\/05186-4"],"award-info":[{"award-number":["2022\/05186-4"]}]},{"name":"The State of S\u00e3o Paulo Research Foundation (FAPESP)","award":["303854\/222-7"],"award-info":[{"award-number":["303854\/222-7"]}]},{"name":"The State of S\u00e3o Paulo Research Foundation (FAPESP)","award":["2021\/12407-4"],"award-info":[{"award-number":["2021\/12407-4"]}]},{"name":"The State of S\u00e3o Paulo Research Foundation (FAPESP)","award":["2022\/05186-4"],"award-info":[{"award-number":["2022\/05186-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one\u2019s own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice.<\/jats:p>","DOI":"10.3390\/s23115196","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T02:57:10Z","timestamp":1685501830000},"page":"5196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An Experimental Analysis on Multicepstral Projection Representation Strategies for Dysphonia Detection"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4003-7791","authenticated-orcid":false,"given":"Rodrigo Colnago","family":"Contreras\u00a0","sequence":"first","affiliation":[{"name":"Department of Computer Science and Statistics, Institute of Biosciences, Letters and Exact Sciences, S\u00e3o Paulo State University, S\u00e3o Jos\u00e9 do Rio Preto 15054-000, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2960-8293","authenticated-orcid":false,"given":"Monique Simplicio","family":"Viana\u00a0","sequence":"additional","affiliation":[{"name":"Federal Institute of S\u00e3o Paulo, S\u00e3o Jos\u00e9 do Rio Preto 15030-070, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6202-0806","authenticated-orcid":false,"given":"Everthon Silva","family":"Fonseca\u00a0","sequence":"additional","affiliation":[{"name":"Federal Institute of S\u00e3o Paulo, S\u00e3o Jos\u00e9 do Rio Preto 15030-070, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7718-8203","authenticated-orcid":false,"given":"Francisco Lledo","family":"dos Santos","sequence":"additional","affiliation":[{"name":"Faculty of Architecture and Engineering, Mato Grosso State University, C\u00e1ceres 78217-900, MT, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4990-0056","authenticated-orcid":false,"given":"Rodrigo Bruno","family":"Zanin\u00a0","sequence":"additional","affiliation":[{"name":"Faculty of Architecture and Engineering, Mato Grosso State University, C\u00e1ceres 78217-900, MT, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0924-8024","authenticated-orcid":false,"given":"Rodrigo Capobianco","family":"Guido\u00a0","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Statistics, Institute of Biosciences, Letters and Exact Sciences, S\u00e3o Paulo State University, S\u00e3o Jos\u00e9 do Rio Preto 15054-000, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5994","DOI":"10.1109\/ACCESS.2018.2889996","article-title":"A survey on biometric authentication: Toward secure and privacy-preserving identification","volume":"7","author":"Rui","year":"2018","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"27721","DOI":"10.1007\/s11042-020-09197-7","article-title":"A review on performance, security and various biometric template protection schemes for biometric authentication systems","volume":"79","author":"Sarkar","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","unstructured":"Sharif, M., Raza, M., Shah, J.H., Yasmin, M., and Fernandes, S.L. (2019). Handbook of Multimedia Information Security: Techniques and Applications, Srpinger."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yudin, O., Ziubina, R., Buchyk, S., Bohuslavska, O., and Teliushchenko, V. (2019, January 2\u20136). Speaker\u2019s Voice Recognition Methods in High-Level Interference Conditions. Proceedings of the 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON), Lviv, Ukraine.","DOI":"10.1109\/UKRCON.2019.8879937"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chandra, E., and Sunitha, C. (2009, January 6\u20137). A review on Speech and Speaker Authentication System using Voice Signal feature selection and extraction. Proceedings of the 2009 IEEE International Advance Computing Conference, Patiala, India.","DOI":"10.1109\/IADCC.2009.4809211"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1121\/1.1937211","article-title":"Voiceprint identification","volume":"34","author":"Kersta","year":"1962","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Senk, C., and Dotzler, F. (2011, January 22\u201326). Biometric authentication as a service for enterprise identity management deployment: A data protection perspective. Proceedings of the 2011 Sixth International Conference on Availability, Reliability and Security, Vienna, Austria.","DOI":"10.1109\/ARES.2011.14"},{"key":"ref_8","first-page":"38","article-title":"A review of voice-base person identification: State-of-the-art","volume":"3","author":"Folorunso","year":"2019","journal-title":"Covenant J. Eng. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khoury, E., El Shafey, L., and Marcel, S. (2014, January 4\u20139). Spear: An open source toolbox for speaker recognition based on Bob. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6853879"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Memon, Q., AlKassim, Z., AlHassan, E., Omer, M., and Alsiddig, M. (2017, January 22\u201324). Audio-visual biometric authentication for secured access into personal devices. Proceedings of the 6th International Conference on Bioinformatics and Biomedical Science.","DOI":"10.1145\/3121138.3121165"},{"key":"ref_11","unstructured":"Tait, B.L. (2011). Global Security, Safety and Sustainability & e-Democracy, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Osman, M.A., Zawawi Talib, A., Sanusi, Z.A., Yen, T.S., and Alwi, A.S. (2011, January 20\u201322). An exploratory study on the trend of smartphone usage in a developing country. Proceedings of the Digital Enterprise and Information Systems: International Conference, DEIS 2011, London, UK.","DOI":"10.1007\/978-3-642-22603-8_35"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, S., and Liu, J. (2011). Recent Application in Biometrics, IntechOpen.","DOI":"10.5772\/970"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1177\/0961000618759414","article-title":"Talk to me: Exploring user interactions with the Amazon Alexa","volume":"51","author":"Lopatovska","year":"2019","journal-title":"J. Librariansh. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, B., Sainath, T.N., Narayanan, A., Caroselli, J., Bacchiani, M., Misra, A., Shafran, I., Sak, H., Pundak, G., and Chin, K.K. (2017, January 20\u201324). Acoustic Modeling for Google Home. Proceedings of the Interspeech, Stockholm, Sweden.","DOI":"10.21437\/Interspeech.2017-234"},{"key":"ref_16","unstructured":"Assefi, M., Liu, G., Wittie, M.P., and Izurieta, C. (2015, January 12\u201314). An experimental evaluation of apple siri and google speech recognition. Proccedings of the 24th International Conference on Software Engineering and Data Engineering, San Diego, CA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kepuska, V., and Bohouta, G. (2018, January 8\u201310). Next-generation of virtual personal assistants (microsoft cortana, apple siri, amazon alexa and google home). Proceedings of the 2018 IEEE 8th annual computing and communication workshop and conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2018.8301638"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1002\/lary.26655","article-title":"Central voice production and pathophysiology of spasmodic dysphonia","volume":"128","author":"Mor","year":"2018","journal-title":"Laryngoscope"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jvoice.2009.10.009","article-title":"Pathophysiology and treatment of muscle tension dysphonia: A review of the current knowledge","volume":"25","author":"Claeys","year":"2011","journal-title":"J. Voice"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/j.otohns.2008.01.014","article-title":"Systematic review of the treatment of functional dysphonia and prevention of voice disorders","volume":"138","author":"Jani","year":"2008","journal-title":"Otolaryngol. Neck Surg."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.ejcdt.2014.03.006","article-title":"Voice changes in patients with chronic obstructive pulmonary disease","volume":"63","author":"Mohamed","year":"2014","journal-title":"Egypt. J. Chest Dis. Tuberc."},{"key":"ref_22","unstructured":"Ngo, Q.C., Motin, M.A., Pah, N.D., Drot\u00e1r, P., Kempster, P., and Kumar, D. (2022). Computer Methods and Programs in Biomedicine, Elsevier."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Little, M., McSharry, P., Hunter, E., Spielman, J., and Ramig, L. (2008). Suitability of dysphonia measurements for telemonitoring of Parkinson\u2019s disease. Nat. Preced.","DOI":"10.1038\/npre.2008.2298.1"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Agbavor, F., and Liang, H. (2023). Artificial Intelligence-Enabled End-To-End Detection and Assessment of Alzheimer\u2019s Disease Using Voice. Brain Sci., 13.","DOI":"10.3390\/brainsci13010028"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1002\/lary.26947","article-title":"Health disparities among adults with voice problems in the United States","volume":"128","author":"Hur","year":"2018","journal-title":"Laryngoscope"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"243.e21","DOI":"10.1016\/j.jvoice.2016.04.005","article-title":"Assessment of grade of dysphonia and correlation with quality of life protocol","volume":"31","author":"Spina","year":"2017","journal-title":"J. Voice"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1002\/lary.29082","article-title":"Hey Siri: How effective are common voice recognition systems at recognizing dysphonic voices?","volume":"131","author":"Rohlfing","year":"2021","journal-title":"Laryngoscope"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Barche, P., Gurugubelli, K., and Vuppala, A.K. (2020, January 25\u201329). Towards Automatic Assessment of Voice Disorders: A Clinical Approach. Proceedings of the INTERSPEECH, Shanghai, China.","DOI":"10.21437\/Interspeech.2020-2160"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e38472","DOI":"10.2196\/38472","article-title":"The Effectiveness of Supervised Machine Learning in Screening and Diagnosing Voice Disorders: Systematic Review and Meta-analysis","volume":"24","author":"Shuweihdi","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"947.e11","DOI":"10.1016\/j.jvoice.2018.07.014","article-title":"A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders","volume":"33","author":"Hegde","year":"2019","journal-title":"J. Voice"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shrivas, A., Deshpande, S., Gidaye, G., Nirmal, J., Ezzine, K., Frikha, M., Desai, K., Shinde, S., Oza, A.D., and Burduhos-Nergis, D.D. (2022). Employing Energy and Statistical Features for Automatic Diagnosis of Voice Disorders. Diagnostics, 12.","DOI":"10.3390\/diagnostics12112758"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"28499","DOI":"10.1007\/s11042-020-09424-1","article-title":"Wavelet sub-band features for voice disorder detection and classification","volume":"79","author":"Gidaye","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"16246","DOI":"10.1109\/ACCESS.2018.2816338","article-title":"Voice disorder identification by using machine learning techniques","volume":"6","author":"Verde","year":"2018","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Dankovi\u010dov\u00e1, Z., Sov\u00e1k, D., Drot\u00e1r, P., and Vokorokos, L. (2018). Machine Learning Approach to Dysphonia Detection. Appl. Sci., 8.","DOI":"10.3390\/app8101927"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"135953","DOI":"10.1109\/ACCESS.2021.3117665","article-title":"A comparison of cepstral features in the detection of pathological voices by varying the input and filterbank of the cepstrum computation","volume":"9","author":"Reddy","year":"2021","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Souissi, N., and Cherif, A. (2015, January 18\u201320). Dimensionality reduction for voice disorders identification system based on mel frequency cepstral coefficients and support vector machine. Proceedings of the 2015 7th International Conference on Modelling, Identification and Control (ICMIC), Sousse, Tunisia.","DOI":"10.1109\/ICMIC.2015.7409479"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lee, J.Y. (2021). Experimental evaluation of deep learning methods for an intelligent pathological voice detection system using the saarbruecken voice database. Appl. Sci., 11.","DOI":"10.3390\/app11157149"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1109\/TIM.2017.2781958","article-title":"Discriminating pathological voice from healthy voice using cepstral peak prominence smoothed distribution in sustained vowel","volume":"67","author":"Castellana","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Castellana, A., Carullo, A., Astolfi, A., Bisetti, M.S., and Colombini, J. (2018, January 11\u201313). Vocal health assessment by means of Cepstral Peak Prominence Smoothed distribution in continuous speech. Proceedings of the 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy.","DOI":"10.1109\/MeMeA.2018.8438655"},{"key":"ref_40","unstructured":"Woldert-Jokisz, B. (2023, May 22). Saarbruecken Voice Database. Available online: https:\/\/stimmdatenbank.coli.uni-saarland.de\/help_en.php4."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"55689","DOI":"10.1109\/ACCESS.2019.2913444","article-title":"Dysphonia detection index (DDI): A new multi-parametric marker to evaluate voice quality","volume":"7","author":"Verde","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.otc.2012.09.001","article-title":"Laryngoscopy, stroboscopy and other tools for the evaluation of voice disorders","volume":"46","author":"Sulica","year":"2013","journal-title":"Otolaryngol. Clin. N. Am."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1002\/lary.23630","article-title":"Diagnostic accuracy of history, laryngoscopy, and stroboscopy","volume":"123","author":"Paul","year":"2013","journal-title":"Laryngoscope"},{"key":"ref_44","unstructured":"Akhlaghi, M., Abedinzadeh, M., Ahmadi, A., and Heidari, Z. (2017). Predicting difficult laryngoscopy and intubation with laryngoscopic exam test: A new method. Acta Med. Iran., 453\u2013458."},{"key":"ref_45","first-page":"13","article-title":"La valutazione soggettiva ed oggettiva della disfonia. Il protocollo SIFEL","volume":"24","author":"Maccarini","year":"2002","journal-title":"Acta Phoniatr. Lat."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.heares.2009.08.011","article-title":"Fundamental frequency and speech intelligibility in background noise","volume":"266","author":"Brown","year":"2010","journal-title":"Hear. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1016\/j.protcy.2014.10.134","article-title":"Accuracy of jitter and shimmer measurements","volume":"16","author":"Teixeira","year":"2014","journal-title":"Procedia Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.procs.2018.10.040","article-title":"Harmonic to noise ratio measurement-selection of window and length","volume":"138","author":"Fernandes","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"142.e5","DOI":"10.1016\/j.jvoice.2020.10.020","article-title":"The usefulness of multi voice evaluation: Development of a model for predicting a degree of dysphonia","volume":"37","author":"Lee","year":"2023","journal-title":"J. Voice"},{"key":"ref_50","unstructured":"Duffy, J.R. (2019). Motor Speech Disorders E-Book: Substrates, Differential Diagnosis, and Management, Elsevier Health Sciences."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.jvoice.2020.11.001","article-title":"The effect of pitch and loudness auditory feedback perturbations on vocal quality during sustained phonation","volume":"37","author":"Schenck","year":"2023","journal-title":"J. Voice"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"ElBouazzaoui, L., Chebbi, S., Idrissi, N., and Jebara, S.B. (2022, January 18\u201320). Relevant pitch features selection for voice disorders families classification. Proceedings of the 2022 11th International Symposium on Signal, Image, Video and Communications (ISIVC), El Jadida, Morocco.","DOI":"10.1109\/ISIVC54825.2022.9800723"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1044\/1092-4388(2001\/027)","article-title":"Acoustic discrimination of pathological voice","volume":"44","author":"Parsa","year":"2001","journal-title":"J. Speech Lang. Hear. Res."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.procs.2017.11.004","article-title":"Vocal acoustic analysis\u2013classification of dysphonic voices with artificial neural networks","volume":"121","author":"Teixeira","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Fernandes, J.F.T., Freitas, D., Junior, A.C., and Teixeira, J.P. (2023). Determination of Harmonic Parameters in Pathological Voices\u2014Efficient Algorithm. Appl. Sci., 13.","DOI":"10.3390\/app13042333"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"101615","DOI":"10.1016\/j.bspc.2019.101615","article-title":"Acoustic investigation of speech pathologies based on the discriminative paraconsistent machine (DPM)","volume":"55","author":"Fonseca","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2030001","DOI":"10.1142\/S0219691320300017","article-title":"CWT\u00d7 DWT\u00d7 DTWT\u00d7 SDTWT: Clarifying terminologies and roles of different types of wavelet transforms","volume":"18","author":"Guido","year":"2020","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"ref_58","unstructured":"Agbinya, J.I. (1996, January 29\u201329). Discrete wavelet transform techniques in speech processing. Proceedings of the Digital Processing Applications (TENCON\u201996), Perth, Australia."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1109\/TBME.2012.2183367","article-title":"Novel speech signal processing algorithms for high-accuracy classification of Parkinson\u2019s disease","volume":"59","author":"Tsanas","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Fonseca, E.S., Pereira, D.C.M., Maschi, L.F.C., Guido, R.C., and Paulo, K.C.S. (2017, January 12\u201314). Linear prediction and discrete wavelet transform to identify pathology in voice signals. Proceedings of the 2017 Signal Processing Symposium (SPSympo), Jachranka, Poland.","DOI":"10.1109\/SPS.2017.8053638"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.irbm.2019.11.004","article-title":"Voice pathologies classification and detection using EMD-DWT analysis based on higher order statistic features","volume":"41","author":"Hammami","year":"2020","journal-title":"IRBM"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.compbiomed.2013.03.006","article-title":"Wavelet adaptation for automatic voice disorders sorting","volume":"43","author":"Saeedi","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_63","unstructured":"Kassim, F.N.C., Vijean, V., Muthusamy, H., Abdullah, Z., Abdullah, R., and Palaniappan, R. (2020, January 26\u201327). DT-CWPT based Tsallis Entropy for Vocal Fold Pathology Detection. Proceedings of the 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.3390\/e16063009","article-title":"Tsallis wavelet entropy and its application in power signal analysis","volume":"16","author":"Chen","year":"2014","journal-title":"Entropy"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1109\/MSP.2005.1550194","article-title":"The dual-tree complex wavelet transform","volume":"22","author":"Selesnick","year":"2005","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Prabakaran, D., and Shyamala, R. (2019, January 21\u201322). A review on performance of voice feature extraction techniques. Proceedings of the 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India.","DOI":"10.1109\/ICCCT2.2019.8824988"},{"key":"ref_67","unstructured":"Martinez, C., and Rufiner, H. (2000, January 23\u201328). Acoustic analysis of speech for detection of laryngeal pathologies. Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No. 00CH37143), Chicago, IL, USA."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"122136","DOI":"10.1109\/ACCESS.2022.3223444","article-title":"Mel Frequency Cepstral Coefficient and its applications: A Review","volume":"10","author":"Abdul","year":"2022","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TBME.2003.820386","article-title":"Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors","volume":"51","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3109\/14015439.2010.528788","article-title":"On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices","volume":"36","author":"Markaki","year":"2011","journal-title":"Logop. Phoniatr. Vocology"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"384e9","DOI":"10.1016\/j.jvoice.2016.09.003","article-title":"Hierarchical classification and system combination for automatically identifying physiological and neuromuscular laryngeal pathologies","volume":"31","author":"Cordeiro","year":"2017","journal-title":"J. Voice"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"7814952","DOI":"10.1155\/2022\/7814952","article-title":"An Analytical Study of Speech Pathology Detection Based on MFCC and Deep Neural Networks","volume":"2022","author":"Zakariah","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Lee, J.N., and Lee, J.Y. (2023). An Efficient SMOTE-Based Deep Learning Model for Voice Pathology Detection. Appl. Sci., 13.","DOI":"10.3390\/app13063571"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.neucom.2015.12.012","article-title":"A Tutorial on Signal Energy and its Applications","volume":"179","author":"Guido","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.inffus.2017.09.006","article-title":"A Tutorial-review on Entropy-based Handcrafted Feature Extraction for Information Fusion","volume":"41","author":"Guido","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.knosys.2016.05.011","article-title":"ZCR-aided Neurocomputing: A study with applications","volume":"105","author":"Guido","year":"2016","journal-title":"Knowl. Based Syst."},{"key":"ref_77","first-page":"1341","article-title":"Enhancing Teager Energy Operator Based on a Novel and Appealing Concept: Signal mass","volume":"356","author":"Guido","year":"2018","journal-title":"J. Frankl. Inst."},{"key":"ref_78","unstructured":"Alim, S.A., and Rashid, N.K.A. (2018). Some Commonly Used Speech Feature Extraction Algorithms, IntechOpen."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Contreras, R.C., Nonato, L.G., Boaventura, M., Boaventura, I.A.G., Coelho, B.G., and Viana, M.S. (2021, January 21\u201323). A New Multi-filter Framework with Statistical Dense SIFT Descriptor for Spoofing Detection in Fingerprint Authentication Systems. Proceedings of the 20th International Conference on Artificial Intelligence and Soft Computing, Virtual.","DOI":"10.1007\/978-3-030-87897-9_39"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1109\/TPAMI.2010.147","article-title":"Sift flow: Dense correspondence across scenes and its applications","volume":"33","author":"Liu","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"117681","DOI":"10.1109\/ACCESS.2022.3218335","article-title":"A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection","volume":"10","author":"Contreras","year":"2022","journal-title":"IEEE Access"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.csl.2017.01.001","article-title":"Constant Q cepstral coefficients: A spoofing countermeasure for automatic speaker verification","volume":"45","author":"Todisco","year":"2017","journal-title":"Comput. Speech Lang."},{"key":"ref_83","unstructured":"Ladefoged, P., and Johnson, K. (2014). A Course in Phonetics, Cengage Learning."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1016\/j.protcy.2014.10.138","article-title":"Jitter, shimmer and HNR classification within gender, tones and vowels in healthy voices","volume":"16","author":"Teixeira","year":"2014","journal-title":"Procedia Technol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yang, S., Zheng, F., Luo, X., Cai, S., Wu, Y., Liu, K., Wu, M., Chen, J., and Krishnan, S. (2014). Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with Parkinson\u2019s disease. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0088825"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1098\/rspb.2011.0829","article-title":"Masculine voices signal men\u2019s threat potential in forager and industrial societies","volume":"279","author":"Puts","year":"2012","journal-title":"Proc. R. Soc. Biol. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1121\/1.3552866","article-title":"The prioritization of voice fundamental frequency or formants in listeners\u2019 assessments of speaker size, masculinity, and attractiveness","volume":"129","author":"Pisanski","year":"2011","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1006\/anbe.2003.2078","article-title":"Anatomical constraints generate honesty: Acoustic cues to age and weight in the roars of red deer stags","volume":"65","author":"Reby","year":"2003","journal-title":"Anim. Behav."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1121\/1.421048","article-title":"Vocal tract length and formant frequency dispersion correlate with body size in rhesus macaques","volume":"102","author":"Fitch","year":"1997","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Sahidullah, M., Kinnunen, T., and Hanil\u00e7i, C. (2015, January 6\u201310). A comparison of features for synthetic speech detection. Proceedings of the 16th Annual Conference of the International Speech Communication Association (INTERSPEECH 2015), Dresden, Germany.","DOI":"10.21437\/Interspeech.2015-472"},{"key":"ref_91","unstructured":"Qi, J., Wang, D., Jiang, Y., and Liu, R. (2009, January 19\u201324). Auditory features based on gammatone filters for robust speech recognition. Proceedings of the 2013 IEEE International Symposium on Circuits and Systems (ISCAS), Taipei, Taiwan."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1121\/1.3508042","article-title":"Frequency bark cepstral coefficients extraction for speech analysis by synthesis","volume":"128","author":"Herrera","year":"2010","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Rao, K.S., Reddy, V.R., and Maity, S. (2015). Language Identification Using Spectral and Prosodic Features, Springer.","DOI":"10.1007\/978-3-319-17163-0"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"56","DOI":"10.3844\/jcssp.2016.56.61","article-title":"Feature Extraction Method for Improving Speech Recognition in Noisy Environments","volume":"12","author":"Zouhir","year":"2016","journal-title":"J. Comput. Sci."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1137\/1035134","article-title":"On the early history of the singular value decomposition","volume":"35","author":"Stewart","year":"1993","journal-title":"SIAM Rev."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Ramachandran, R., Ravichandran, G., and Raveendran, A. (2020, January 11\u201313). Evaluation of dimensionality reduction techniques for big data. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC48092.2020.ICCMC-00043"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Tanwar, S., Ramani, T., and Tyagi, S. (September, January 31). Dimensionality reduction using PCA and SVD in big data: A comparative case study. Proceedings of the Future Internet Technologies and Trends: First International Conference, ICFITT 2017, Surat, India.","DOI":"10.1007\/978-3-319-73712-6_12"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_100","unstructured":"Grimm, L.G., and Yarnold, P.R. (1995). Reading and Understanding Multivariate Statistics, American Psychological Association."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"38","DOI":"10.2214\/AJR.18.20224","article-title":"Peering into the black box of artificial intelligence: Evaluation metrics of machine learning methods","volume":"212","author":"Handelman","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"ref_102","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_103","unstructured":"Malek, A., Titeux, H., Borzi, S., Nielsen, C.H., Stoter, F.R., Bredin, H., and Moerman, K.M. (2023, May 22). SuperKogito-Spafe: v0.3.2, 2023. Available online: https:\/\/doi.org\/10.5281\/zenodo.7686438."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.wocn.2018.07.001","article-title":"Introducing parselmouth: A python interface to praat","volume":"71","author":"Jadoul","year":"2018","journal-title":"J. Phon."},{"key":"ref_105","unstructured":"Contreras, R.C. (2023, May 25). Result Dataset for Our Experimental Analysis on Multi-Cepstral Projection Representation Strategies for Dysphonia Detection, 2023. Available online: https:\/\/doi.org\/10.5281\/zenodo.7897603."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5196\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:45:05Z","timestamp":1760125505000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/11\/5196"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,30]]},"references-count":105,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23115196"],"URL":"https:\/\/doi.org\/10.3390\/s23115196","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,30]]}}}