{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:28:06Z","timestamp":1740122886886,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T00:00:00Z","timestamp":1696550400000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16815-7","type":"journal-article","created":{"date-parts":[[2023,10,6]],"date-time":"2023-10-06T15:01:41Z","timestamp":1696604501000},"page":"38909-38929","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An audio-based anger detection algorithm using a hybrid artificial neural network and fuzzy logic model"],"prefix":"10.1007","volume":"83","author":[{"given":"Arihant","family":"Surana","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manish","family":"Rathod","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shilpa","family":"Gite","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shruti","family":"Patil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7161-2109","authenticated-orcid":false,"given":"Ganeshsree","family":"Selvachandran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shio Gai","family":"Quek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajith","family":"Abraham","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,6]]},"reference":[{"key":"16815_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2043155.2043156","volume":"2011","author":"P Yaffe","year":"2011","unstructured":"Yaffe P (2011) The 7% rule: fact, fiction, or misunderstanding. Ubiquity 2011:1. https:\/\/doi.org\/10.1145\/2043155.2043156","journal-title":"Ubiquity"},{"issue":"2","key":"16815_CR2","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1109\/5.18626","volume":"77","author":"LR Rabiner","year":"1989","unstructured":"Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257\u2013286. https:\/\/doi.org\/10.1109\/5.18626","journal-title":"Proc IEEE"},{"issue":"4","key":"16815_CR3","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/S0167-6393(03)00099-2","volume":"41","author":"TL Nwe","year":"2003","unstructured":"Nwe TL, Foo SW, De Silva LC (2003) Speech emotion recognition using hidden Markov models. Speech Commun 41(4):603\u2013623","journal-title":"Speech Commun"},{"issue":"4","key":"16815_CR4","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.3390\/s21041249","volume":"21","author":"BJ Abbaschian","year":"2021","unstructured":"Abbaschian BJ, Sierra-Sosa D, Elmaghraby A (2021) Deep learning techniques for speech emotion recognition, from databases to models. Sensors 21(4):1249","journal-title":"Sensors"},{"key":"16815_CR5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s10772-018-9491-z","volume":"21","author":"M Swain","year":"2018","unstructured":"Swain M, Routray A, Kabisatpathy P (2018) Databases, features and classifiers for speech emotion recognition: a review. Int J Speech Technol 21:93\u2013120","journal-title":"Int J Speech Technol"},{"issue":"1","key":"16815_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.11120\/beej.2014.00022","volume":"22","author":"S Voelkel","year":"2014","unstructured":"Voelkel S, Mello LV (2014) Audio feedback \u2013 Better feedback? Bioscience Education 22(1):16\u201330","journal-title":"Bioscience Education"},{"key":"16815_CR7","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 MB, O\u011fuz K (2020) Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun 116:56\u201376","journal-title":"Speech Commun"},{"issue":"3","key":"16815_CR8","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 MS, Karray F (2011) Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recogn 44(3):572\u2013587","journal-title":"Pattern Recogn"},{"issue":"2","key":"16815_CR9","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/s10772-012-9139-3","volume":"15","author":"SG Koolagudi","year":"2012","unstructured":"Koolagudi SG, Rao KS (2012) Emotion recognition from speech using source, system, and prosodic features. Int J Speech Technol 15(2):265\u2013289","journal-title":"Int J Speech Technol"},{"issue":"6","key":"16815_CR10","doi-asserted-by":"publisher","first-page":"1154","DOI":"10.1016\/j.dsp.2012.05.007","volume":"22","author":"L Chen","year":"2012","unstructured":"Chen L, Mao X, Xue Y, Cheng LL (2012) Speech emotion recognition: Features and classification models. Digit Signal Process 22(6):1154\u20131160","journal-title":"Digit Signal Process"},{"key":"16815_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2020.100424","volume":"20","author":"S Langari","year":"2020","unstructured":"Langari S, Marvi H, Zahedi M (2020) Efficient speech emotion recognition using modified feature extraction. Inf Med Unlocked 20:100424","journal-title":"Inf Med Unlocked"},{"issue":"8","key":"16815_CR12","doi-asserted-by":"publisher","first-page":"2203","DOI":"10.1109\/TMM.2014.2360798","volume":"16","author":"Q Mao","year":"2014","unstructured":"Mao Q, Dong M, Huang Z, Zhan Y (2014) Learning salient features for speech emotion recognition using convolutional neural networks. IEEE Trans Multimed 16(8):2203\u20132213","journal-title":"IEEE Trans Multimed"},{"issue":"10","key":"16815_CR13","doi-asserted-by":"publisher","first-page":"1440","DOI":"10.1109\/LSP.2018.2860246","volume":"25","author":"M Chen","year":"2018","unstructured":"Chen M, He X, Yang J, Zhang H (2018) 3-D convolutional recurrent neural networks with attention model for speech emotion recognition. IEEE Signal Process Lett 25(10):1440\u20131444","journal-title":"IEEE Signal Process Lett"},{"issue":"5","key":"16815_CR14","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1016\/j.chb.2010.10.027","volume":"27","author":"JH Yeh","year":"2011","unstructured":"Yeh JH, Pao TL, Lin CY, Tsai YW, Chen YT (2011) Segment-based emotion recognition from continuous Mandarin Chinese speech. Comput Hum Behav 27(5):1545\u20131552","journal-title":"Comput Hum Behav"},{"issue":"13","key":"16815_CR15","doi-asserted-by":"publisher","first-page":"5858","DOI":"10.1016\/j.eswa.2014.03.026","volume":"41","author":"CS Ooi","year":"2014","unstructured":"Ooi CS, Seng KP, Ang L, Chew LW (2014) A new approach of audio emotion recognition. Expert Syst Appl 41(13):5858\u20135869","journal-title":"Expert Syst Appl"},{"issue":"1","key":"16815_CR16","doi-asserted-by":"publisher","first-page":"28","DOI":"10.7763\/JACN.2014.V2.76","volume":"2","author":"S Demircan","year":"2014","unstructured":"Demircan S, Kahramanl\u0131 H (2014) Feature extraction from speech data for emotion recognition. J Adv Comput Netw 2(1):28\u201330","journal-title":"J Adv Comput Netw"},{"issue":"2","key":"16815_CR17","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TSA.2004.838534","volume":"13","author":"CM Lee","year":"2005","unstructured":"Lee CM, Narayanan SS (2005) Toward detecting emotions in spoken dialogs. IEEE Trans Speech Audio Process 13(2):293\u2013303","journal-title":"IEEE Trans Speech Audio Process"},{"key":"16815_CR18","doi-asserted-by":"publisher","unstructured":"Neiberg, D, Elenius, K, Laskowski, K (2006) Emotion recognition in spontaneous speech using GMMs. Proceedings of the Ninth International Conference on Spoken Language Processing (INTERSPEECH 2006 \u2013 ICSLP), 809\u2013812. https:\/\/doi.org\/10.21437\/Interspeech.2006-277","DOI":"10.21437\/Interspeech.2006-277"},{"issue":"1","key":"16815_CR19","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.csl.2014.01.003","volume":"29","author":"H Cao","year":"2015","unstructured":"Cao H, Verma R, Nenkova A (2015) Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech. Comput Speech Lang 29(1):186\u2013202","journal-title":"Comput Speech Lang"},{"issue":"3","key":"16815_CR20","doi-asserted-by":"publisher","first-page":"556","DOI":"10.1016\/j.csl.2010.10.001","volume":"25","author":"EM Albornoz","year":"2011","unstructured":"Albornoz EM, Milone DH, Rufiner HL (2011) Spoken emotion recognition using hierarchical classifiers. Comput Speech Lang 25(3):556\u2013570","journal-title":"Comput Speech Lang"},{"key":"16815_CR21","doi-asserted-by":"publisher","unstructured":"Nikopoulou, R, Vernikos, I, Spyrou, E, Mylonas, P (2018) Emotion recognition from speech: A classroom experiment. Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference (PETRA '18), 104\u2013105, Corfu, Greece. https:\/\/doi.org\/10.1145\/3197768.3197782","DOI":"10.1145\/3197768.3197782"},{"issue":"5","key":"16815_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196391","volume":"13","author":"SR Livingstone","year":"2018","unstructured":"Livingstone SR, Russo FA (2018) The Ryerson audio-visual database of emotional speech and song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. PLoS ONE 13(5):e0196391","journal-title":"PLoS ONE"},{"issue":"4","key":"16815_CR23","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1109\/TAFFC.2014.2336244","volume":"5","author":"H Cao","year":"2014","unstructured":"Cao H, Cooper DG, Keutmann MK, Gur RC, Nenkova A, Verma R (2014) CREMA-D: Crowd-sourced emotional multimodal actors dataset. IEEE Trans Affect Comput 5(4):377\u2013390","journal-title":"IEEE Trans Affect Comput"},{"key":"16815_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-16294-w","author":"W Lee","year":"2023","unstructured":"Lee W, Son G (2023) Investigation of human state classification via EEG signals elicited by emotional audio-visual stimulation. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-16294-w","journal-title":"Multimed Tools Appl"},{"key":"16815_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108396","volume":"103","author":"S Kumar","year":"2022","unstructured":"Kumar S, Gupta SK, Kumar V, Kumar M, Chaube MK, Naik NS (2022) Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19. Comput Electr Eng 103:108396","journal-title":"Comput Electr Eng"},{"key":"16815_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107109","volume":"226","author":"S Kumar","year":"2022","unstructured":"Kumar S, Chaube MK, Alsamhi SH, Gupta SK, Guizani M, Gravina R, Fortino G (2022) A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques. Comput Methods Programs Biomed 226:107109","journal-title":"Comput Methods Programs Biomed"},{"key":"16815_CR27","doi-asserted-by":"publisher","first-page":"1987","DOI":"10.1109\/TASLP.2021.3082307","volume":"29","author":"K Koutini","year":"2021","unstructured":"Koutini K, Zadeh HE, Widmer G (2021) Receptive field regularization techniques for audio classification and tagging with deep convolutional neural networks. IEEE\/ACM Trans Audio, Speech, Lang Process 29:1987\u20132000","journal-title":"IEEE\/ACM Trans Audio, Speech, Lang Process"},{"key":"16815_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2021.03.007","volume":"146","author":"L Schoneveld","year":"2021","unstructured":"Schoneveld L, Othmani A, Abdelkawy H (2021) Leveraging recent advances in deep learning for audio-Visual emotion recognition. Pattern Recogn Lett 146:1\u20137","journal-title":"Pattern Recogn Lett"},{"key":"16815_CR29","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2022.3220190","author":"P Nemani","year":"2022","unstructured":"Nemani P, Krishna GS, Sai BDS, Kumar S (2022) Deep learning based holistic speaker independent visual speech recognition. IEEE Trans Artif Intell. https:\/\/doi.org\/10.1109\/TAI.2022.3220190","journal-title":"IEEE Trans Artif Intell"},{"key":"16815_CR30","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3200060","author":"J Tian","year":"2022","unstructured":"Tian J, She Y (2022) A visual-audio-based emotion recognition system integrating dimensional analysis. IEEE Trans Comput Soc Syst. https:\/\/doi.org\/10.1109\/TCSS.2022.3200060","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"16815_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2022.3228649","author":"Y Khurana","year":"2022","unstructured":"Khurana Y, Gupta S, Sathyaraj R, Raja SP (2022) A multimodal speech emotion recognition system with speaker recognition for social interactions. IEEE Trans Comput Soc Syst. https:\/\/doi.org\/10.1109\/TCSS.2022.3228649","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"16815_CR32","doi-asserted-by":"publisher","unstructured":"Kumar, S, Jaiswal, S, Kumar, R, Singh, SK (2018) Emotion recognition using facial expression. In R. Pal (Ed.), Innovative Research in Attention Modeling and Computer Vision Applications (pp. 327\u2013345). IGI Global. https:\/\/doi.org\/10.4018\/978-1-4666-8723-3.ch013","DOI":"10.4018\/978-1-4666-8723-3.ch013"},{"key":"16815_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104894","volume":"85","author":"D Nandini","year":"2023","unstructured":"Nandini D, Yadav J, Rani A, Singh V (2023) Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms. Biomed Signal Process Control 85:104894","journal-title":"Biomed Signal Process Control"},{"key":"16815_CR34","doi-asserted-by":"publisher","first-page":"5500","DOI":"10.1007\/s00034-023-02367-6","volume":"42","author":"K Chauhan","year":"2023","unstructured":"Chauhan K, Sharma KK, Varma T (2023) Improved Speech emotion recognition using channel-wise global head pooling (CwGHP). Circ Syst Signal Process 42:5500\u20135522","journal-title":"Circ Syst Signal Process"},{"key":"16815_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2023.104676","volume":"133","author":"B Mocanu","year":"2023","unstructured":"Mocanu B, Tapu R, Zaharia T (2023) Multimodal emotion recognition using cross modal audio-video fusion with attention and deep metric learning. Image Vis Comput 133:104676","journal-title":"Image Vis Comput"},{"key":"16815_CR36","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.inffus.2023.03.015","volume":"96","author":"C Min","year":"2023","unstructured":"Min C, Lin H, Li X, Zhao H, Lu J, Yang L, Xu B (2023) Finding hate speech with auxiliary emotion detection from self-training multi-label learning perspective. Inf Fus 96:214\u2013223","journal-title":"Inf Fus"},{"key":"16815_CR37","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neucom.2022.04.049","volume":"493","author":"Y Li","year":"2022","unstructured":"Li Y, Kazemeini A, Mehta Y, Cambria E (2022) Multitask learning for emotion and personality traits detection. Neurocomputing 493:340\u2013350","journal-title":"Neurocomputing"},{"key":"16815_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104624","volume":"83","author":"A Pradhan","year":"2023","unstructured":"Pradhan A, Srivastava S (2023) Hierarchical extreme puzzle learning machine-based emotion recognition using multimodal physiological signals. Biomed Signal Process Control 83:104624","journal-title":"Biomed Signal Process Control"},{"key":"16815_CR39","volume":"17","author":"N Ahmed","year":"2023","unstructured":"Ahmed N, Angbari ZA, Girijia S (2023) A systematic survey on multimodal emotion recognition using learning algorithms. Intell Syst Appl 17:200171","journal-title":"Intell Syst Appl"},{"key":"16815_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14885-1","author":"M Firdaus","year":"2023","unstructured":"Firdaus M, Singh GV, Ekbal A, Bhattacharyya P (2023) Affect-GCN: a multimodal graph convolutional network for multi-emotion with intensity recognition and sentiment analysis in dialogues. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-14885-1","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16815-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16815-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16815-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T10:42:14Z","timestamp":1712140934000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16815-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":40,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["16815"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16815-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,10,6]]},"assertion":[{"value":"11 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"(i). Authors\u2019 declaration: This manuscript is the authors' original work and has not been published elsewhere. All authors have checked the manuscript and have agreed to this submission.(ii). Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Compliance"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}