{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:41:35Z","timestamp":1764225695369,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"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>We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model\u2014support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker\u2019s gender and his\/her identity. The proposed architecture works in a semi-sequential manner consisting of two stages: the first classifier exploits the prosodic features to determine the speaker\u2019s gender which in turn is used with the short-term spectral features as inputs to the second classifier system in order to identify the speaker. The second classifier system employs two types of short-term spectral features; namely mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) as well as gender information as inputs to two different classifiers (GMM and GMM supervector-based SVM) which in total leads to construction of four classifiers. The outputs from the second stage classifiers; namely GMM-MFCC maximum likelihood classifier (MLC), GMM-GFCC MLC, GMM-MFCC supervector SVM, and GMM-GFCC supervector SVM are fused at score level by the weighted Borda count approach. The weight factors are computed on the fly via Mamdani fuzzy inference system that its inputs are the signal to noise ratio and the length of utterance. Experimental evaluations suggest that the proposed architecture and the fusion framework are promising and can improve the recognition performance of the system in challenging environments where the signal-to-noise ratio is low, and the length of utterance is short; such scenarios often arise in social robot interactions with humans.<\/jats:p>","DOI":"10.3390\/s21155097","type":"journal-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T05:23:48Z","timestamp":1627449828000},"page":"5097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation"],"prefix":"10.3390","volume":"21","author":[{"given":"Mohammad","family":"Al-Qaderi","sequence":"first","affiliation":[{"name":"Department of Mechatronics Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elfituri","family":"Lahamer","sequence":"additional","affiliation":[{"name":"Autonomous and Intelligent Systems Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad","family":"Rad","sequence":"additional","affiliation":[{"name":"Autonomous and Intelligent Systems Laboratory, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.5334\/pb.ap","article-title":"Person Recognition Is Easier from Faces than from Voices","volume":"54","author":"Barsics","year":"2014","journal-title":"Psychol. Belg."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Benesty, J., Sondhi, M.M., and Huang, Y.A. (2008). Overview of Speaker Recognition BT. Springer Handbook of Speech Processing, Springer.","DOI":"10.1007\/978-3-540-49127-9"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.specom.2009.08.009","article-title":"An Overview of Text-Independent Speaker Recognition: From Features to Supervectors","volume":"52","author":"Kinnunen","year":"2010","journal-title":"Speech Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"13863","DOI":"10.1007\/s00521-020-04793-y","article-title":"Robust Features for Text-Independent Speaker Recognition with Short Utterances","volume":"32","author":"Chakroun","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Mitra, V., Franco, H., Stern, R.M., van Hout, J., Ferrer, L., Graciarena, M., Wang, W., Vergyri, D., Alwan, A., and Hansen, J.H.L. (2017). Robust Features in Deep-Learning-Based Speech Recognition. New Era for Robust Speech Recognition, Springer International Publishing.","DOI":"10.1007\/978-3-319-64680-0_8"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20","DOI":"10.18178\/ijmlc.2019.9.1.760","article-title":"Speaker Verification Using Deep Neural Networks: A Review","volume":"9","author":"Irum","year":"2019","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TASL.2011.2125954","article-title":"Speaker diarization: A Review of Recent Research","volume":"20","author":"Anguera","year":"2012","journal-title":"IEEE Trans. Audio Speech. Lang. Process."},{"key":"ref_8","unstructured":"Evans, N., Kinnunen, T., Yamagishi, J., Wu, Z., Alegre, F., and De Leon, P. (2014). Handbook of Biometric Anti-Spoofing. Advances in Computer Vision and Pattern Recognition, Springer."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/MSP.2015.2462851","article-title":"Speaker Recognition by Machines and Humans: A Tutorial Review","volume":"32","author":"Hansen","year":"2015","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/TASL.2008.925147","article-title":"A Study of Interspeaker Variability in Speaker Verification","volume":"16","author":"Kenny","year":"2008","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.eswa.2017.08.015","article-title":"Speaker Identification Features Extraction Methods: A Systematic Review","volume":"90","author":"Tirumala","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Niculescu, A., Van Dijk, B., Nijholt, A., Limbu, D.K., See, S.L., and Wong, A.H.Y. (2010). Socializing with Olivia, the Youngest Robot Receptionist Outside the Lab. Social Robotics, Springer.","DOI":"10.1007\/978-3-642-17248-9_6"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1080\/0952813X.2019.1653383","article-title":"Multi-Modal Classifier Fusion with Feature Cooperation for Glaucoma Diagnosis","volume":"31","author":"Benzebouchi","year":"2019","journal-title":"J. Exp. Theor. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"32187","DOI":"10.1109\/ACCESS.2020.2973541","article-title":"Text-Independent Speaker Identification through Feature Fusion and Deep Neural Network","volume":"8","author":"Jahangir","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.neucom.2020.04.041","article-title":"A Network Model of Speaker Identification with New Feature Extraction Methods and Asymmetric BLSTM","volume":"403","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ahmad, K.S., Thosar, A.S., Nirmal, J.H., and Pande, V.S. (2015, January 2). A Unique Approach in Text Independent Speaker Recognition Using MFCC Feature Sets and Probabilistic Neural Network. Proceedings of the ICAPR 2015: 2015 Eighth International Conference on Advances in Pattern Recognition, Kolkata, India.","DOI":"10.1109\/ICAPR.2015.7050669"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"224","DOI":"10.5755\/j01.itc.49.2.22258","article-title":"One Solution of Extension of Mel-Frequency Cepstral Coefficients Feature Vector for Automatic Speaker Recognition","volume":"49","year":"2020","journal-title":"Inf. Technol. Control"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/89.365379","article-title":"Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models","volume":"3","author":"Reynolds","year":"1995","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/LSP.2006.870086","article-title":"Support Vector Machines Using GMM Supervectors for Speaker Verification","volume":"13","author":"Campbell","year":"2006","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1109\/TASL.2010.2064307","article-title":"Front End Factor Analysis for Speaker Verification","volume":"19","author":"Dehak","year":"2011","journal-title":"IEEE Trans. Audio Speech. Lang. Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dehak, N., Kenny, P.J., Dehak, R., Glembek, O., Dumouchel, P., Burget, L., Hubeika, V., and Castaldo, F. (2009, January 26). Support Vector Machines and Joint Factor Analysis for Speaker Verification. Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, Taiwan.","DOI":"10.1109\/ICASSP.2009.4960564"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Latif, S., Cuay\u00e1huitl, H., Pervez, F., Shamshad, F., Ali, H.S., and Cambria, E. (2021). A Survey on Deep Reinforcement Learning for Audio-Based Applications. arXiv.","DOI":"10.1007\/s10462-022-10224-2"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ye, F., and Yang, J. (2021). A Deep Neural Network Model for Speaker Identification. Appl. Sci., 11.","DOI":"10.3390\/app11083603"},{"key":"ref_24","unstructured":"Pelecanos, J., and Sridharan, S. (2001, January 18\u201322). Feature Warping for Robust Speaker Verification. Proceedings of the 2001 A Speaker Odyssey: The Speaker Recognition Workshop, Crete, Greece."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1109\/TASSP.1981.1163530","article-title":"Cepstral Analysis Technique for Automatic Speaker Verification","volume":"29","author":"Furui","year":"1981","journal-title":"IEEE Trans. Acoust."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hatch, A.O., Kajarekar, S., and Stolcke, A. (2006, January 17\u201321). Within-Class Covariance Normalization for SVM-Based Speaker Recognition. Proceedings of the Ninth International Conference on Spoken Language Processing, Pittsburgh, PA, USA.","DOI":"10.21437\/Interspeech.2006-183"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1006\/dspr.1999.0361","article-title":"Speaker Verification Using Adapted Gaussian Mixture Models","volume":"10","author":"Reynolds","year":"2000","journal-title":"Digit. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum Likelihood from Incomplete Data Via the EM Algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1016\/j.compeleceng.2012.09.014","article-title":"Investigation of the Effect of Data Duration and Speaker Gender on Text-Independent Speaker Recognition","volume":"39","year":"2013","journal-title":"Comput. Electr. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Al-Kaltakchi, M.T.S., Woo, W.L., Dlay, S.S., and Chambers, J.A. (September, January 28). Comparison of I-Vector and GMM-UBM Approaches to Speaker Identification with Timit and NIST 2008 Databases in Challenging Environments. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO), Kos, Greece.","DOI":"10.23919\/EUSIPCO.2017.8081264"},{"key":"ref_31","unstructured":"Roger, V., Farinas, J., and Pinquier, J. (2020). Deep Neural Networks for Automatic Speech Processing: A Survey from Large Corpora to Limited Data. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bai, Z., Zhang, X.L., and Chen, J. (2020). Speaker Recognition Based on Deep Learning: An Overview. arXiv.","DOI":"10.1016\/j.neunet.2021.03.004"},{"key":"ref_33","unstructured":"Sztah\u00f3, D., Szasz\u00e1k, G., and Beke, A. (2019). Deep Learning Methods in Speaker Recognition: A Review. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1207\/s15327051hci1901&2_4","article-title":"Interactive Robots as Social Partners and Peer Tutors for Children: A Field Trial","volume":"19","author":"Kanda","year":"2004","journal-title":"Hum. Comput. Interact."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gross, H., Schr\u00f6ter, C., Mueller, S., Volkhardt, M., Einhorn, E., Bley, A., Langner, T., Martin, C., and Merten, M. (2011, January 9\u201312). I\u2019ll Keep an Eye on You: Home Robot Companion for Elderly People with Cognitive Impairment. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA.","DOI":"10.1109\/ICSMC.2011.6084050"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s12369-013-0220-0","article-title":"Domestic Robots for Older Adults: Attitudes, Preferences, and Potential","volume":"6","author":"Smarr","year":"2013","journal-title":"Int. J. Soc. Robot."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Poddar, A., Sahidullah, M., and Saha, G. (2015, January 17\u201320). Performance Comparison of Speaker Recognition Systems in Presence of Duration Variability. Proceedings of the 2015 Annual IEEE India Conference (INDICON), New Delhi, India.","DOI":"10.1109\/INDICON.2015.7443464"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.specom.2014.10.005","article-title":"Spoofing and Countermeasures for Speaker Verification: A Survey","volume":"66","author":"Wu","year":"2015","journal-title":"Speech Commun."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rao, K.S., and Sarkar, S. (2014). Robust Speaker Verification: A Review. Robust Speaker Recognition in Noisy Environments, Springer International Publishing.","DOI":"10.1007\/978-3-319-07130-5"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"101199","DOI":"10.1016\/j.csl.2021.101199","article-title":"Adversarial Attack and Defense Strategies for Deep Speaker Recognition Systems","volume":"68","author":"Jati","year":"2021","journal-title":"Comput. Speech Lang."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Pandey, L., Chaudhary, K., and Hegde, R.M. (2017, January 2\u20134). Fusion of Spectral and Prosodic Information Using Combined Error Optimization for Keyword Spotting. Proceedings of the 2017 Twenty-third National Conference on Communications (NCC), Chennai, India.","DOI":"10.1109\/NCC.2017.8077071"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/1687-4722-2013-8","article-title":"Evaluation of Influence of Spectral and Prosodic Features on GMM Classification of Czech and Slovak Emotional Speech","volume":"2013","year":"2013","journal-title":"Eurasip J. Audio Speech Music Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.compeleceng.2016.06.002","article-title":"A New Approach with Score-Level Fusion for the Classification of a Speaker Age and Gender","volume":"53","author":"Nabiyev","year":"2016","journal-title":"Comput. Electr. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kockmann, M., Ferrer, L., Burget, L., and \u010cernock\u1ef3, J. (2011, January 27\u201331). iVector Fusion of Prosodic and Cepstral Features for Speaker Verification. Proceedings of the INTERSPEECH 2011: 12th Annual Conference of the International Speech Communication Association, Florence, Italy.","DOI":"10.21437\/Interspeech.2011-57"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1002\/sec.308","article-title":"Pitch-Based Gender Identification with Two-Stage Classification","volume":"5","author":"Hu","year":"2012","journal-title":"Secur. Commun. Netw."},{"key":"ref_46","unstructured":"Reynolds, D.A., Zissman, M., Quatieri, T.F., O\u2019Leary, G., and Carlson, B.A. (1995, January 9\u201312). The Effects of Telephone Transmission Degradations on Speaker Recognition Performance. Proceedings of the 1995 International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, USA."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/MCAS.2011.941079","article-title":"An Overview of Speaker Identification: Accuracy and Robustness Issues","volume":"11","author":"Togneri","year":"2011","journal-title":"IEEE Circuits Syst. Mag."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1109\/TASL.2008.2010882","article-title":"Speaker Model Clustering for Efficient Speaker Identification in Large Population Applications","volume":"17","author":"Apsingekar","year":"2009","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_49","first-page":"126","article-title":"Improving Speaker Recognition by Biometric Voice Deconstruction","volume":"3","year":"2015","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0020-7373(75)80002-2","article-title":"An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller","volume":"7","author":"Mamdani","year":"1975","journal-title":"Int. J. Man. Mach. Stud."},{"key":"ref_51","unstructured":"Patterson, R.D., Nimmo-Smith, I., Holdsworth, J., and Rice, P. (2021, July 15). An Efficient Auditory Filterbank Based on the Gammatone Function. Available online: https:\/\/www.pdn.cam.ac.uk\/system\/files\/documents\/SVOSAnnexB1988.pdf."},{"key":"ref_52","unstructured":"Moore, B.C.J. (1997). An Introduction to the Psychology of Hearing, Academic Press. [4th ed.]."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Patterson, R.D., Holdsworth, J., and Allerhand, M. (1992). Auditory Models as Preprocessors for Speech Recognition. The Auditory Processing of Speech: From Sounds to Words, Mouton de Gruyter.","DOI":"10.1515\/9783110879018.67"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1016\/j.specom.2006.09.001","article-title":"Periodicity Estimation in Synthesized Phonation Signals Using Cepstral Rahmonic Peaks","volume":"48","author":"Murphy","year":"2006","journal-title":"Speech Commun."},{"key":"ref_55","unstructured":"Shue, Y.-L. (2010). The Voice Source in Speech Production: Data, Analysis and Models, University of California."},{"key":"ref_56","unstructured":"Lartillot, O., and Toiviainen, P. (2007, January 10\u201315). A Matlab Toolbox for Musical Feature Extraction from Audio. Proceedings of the International Conference on Digital Audio Effects, Bordeaux, France."},{"key":"ref_57","first-page":"341","article-title":"Speak and Unspeak with Praat","volume":"5","author":"Boersma","year":"2001","journal-title":"Glot Int."},{"key":"ref_58","unstructured":"Platt, J. (1999). Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Advances in Large Margin Classifiers, MIT Press."},{"key":"ref_59","unstructured":"Cristianini, N., and Shawe-Taylor, J. (2012). An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods, Cambridge University Press."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.csl.2005.06.003","article-title":"Support vector machines for speaker and language recognition","volume":"20","author":"Campbell","year":"2006","journal-title":"Comput. Speech Lang."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Vapnik, V.N. (2000). The Nature of Statistical Learning Theory, Springer. [2nd ed.].","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref_62","unstructured":"Leonard, R.G., and Doddington, G. (2021, July 15). TIDIGITS LDC93S10. Web Download. Philadelphia: Linguistic Data Consortium. Available online: https:\/\/catalog.ldc.upenn.edu\/LDC93S10."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Lee, M.K., Forlizzi, J., Rybski, P.E., Crabbe, F., Chung, W., Finkle, J., Glaser, E., and Kiesler, S. (2009, January 9\u201313). The Snackbot: Documenting the Design of a Robot for Long-term Human-Robot Interaction. Proceedings of the 4th ACM\/IEEE International Conference on Human Robot Interaction, La Jolla, CA, USA.","DOI":"10.1145\/1514095.1514100"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Gross, H., Schroeter, C., Mueller, S., Volkhardt, M., Einhorn, E., Bley, A., Langner, T., Merten, M., Huijnen, C., and van den Heuvel, H. (2012, January 14\u201317). Further Progress towards a Home Robot Companion for People with Mild Cognitive Impairment. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetic, Seoul, Korea.","DOI":"10.1109\/ICSMC.2012.6377798"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Kozhirbayev, Z., Erol, B.A., Sharipbay, A., and Jamshidi, M. (2018, January 3\u20136). Speaker Recognition for Robotic Control via an IoT Device. Proceedings of the 2018 World Automation Congress (WAC), Stevenson, WA, USA.","DOI":"10.23919\/WAC.2018.8430295"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1109\/TASL.2012.2205242","article-title":"A CASA-Based System for Long-Term SNR Estimation","volume":"20","author":"Narayanan","year":"2012","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Islam, M.A., Jassim, W.A., Cheok, N.S., and Zilany, M.S.A. (2016). A Robust Speaker Identification System Using the Responses from a Model of the Auditory Periphery. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0158520"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5097\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:36:02Z","timestamp":1760164562000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/15\/5097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,28]]},"references-count":67,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21155097"],"URL":"https:\/\/doi.org\/10.3390\/s21155097","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,7,28]]}}}