{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:42:35Z","timestamp":1772556155944,"version":"3.50.1"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"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\/s11042-024-19456-6","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T03:10:15Z","timestamp":1718593815000},"page":"14623-14661","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Audio-visual expression-based emotion recognition model for neglected people in real-time: a late-fusion approach"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6045-5352","authenticated-orcid":false,"given":"Sirshendu","family":"Hore","sequence":"first","affiliation":[]},{"given":"Tanmay","family":"Bhattacharya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"19456_CR1","doi-asserted-by":"publisher","unstructured":"McNally RJ (1999) Handbook of cognition and emotion, British 590 J Psychiatry 176(5). https:\/\/doi.org\/10.1002\/0470013494","DOI":"10.1002\/0470013494"},{"key":"19456_CR2","doi-asserted-by":"publisher","unstructured":"Yang N, Dey N, Sherratt S, Shi F (2019) Emotional state recognition for AI smart home assistants using Mel-frequency Cepstral coefficient features.\u00a0J Intell Fuzzy Syst 39(2):1925\u20131936. https:\/\/doi.org\/10.3233\/JIFS179963","DOI":"10.3233\/JIFS179963"},{"issue":"6","key":"19456_CR3","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1037\/h0077714","volume":"39","author":"J Russell","year":"1980","unstructured":"Russell J (1980) A circumplex model of affect. J Person Soc Psychol 39(6):1161\u20131178. https:\/\/doi.org\/10.1037\/h0077714","journal-title":"J Person Soc Psychol"},{"issue":"1","key":"19456_CR4","first-page":"71","volume":"7","author":"AT Latinjak","year":"2012","unstructured":"Latinjak AT (2012) The underlying structure of emotions: A tri-dimensional model of core affect and emotion concepts for sports. Rev Iberoam Psicol Ejerc Deporte 7(1):71\u201388","journal-title":"Rev Iberoam Psicol Ejerc Deporte"},{"key":"19456_CR5","doi-asserted-by":"crossref","unstructured":"Cambria E, Livingstone A, Hussain A (2012) The hourglass of emotions, in: Cognitive Behavioural Systems, Springer, pp 144\u2013157","DOI":"10.1007\/978-3-642-34584-5_11"},{"issue":"4","key":"19456_CR6","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1511\/2001.28.344","volume":"89","author":"R Plutchik","year":"2001","unstructured":"Plutchik R (2001) The nature of emotions: Human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am Sci 89(4):344\u2013350","journal-title":"Am Sci"},{"key":"19456_CR7","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.1007\/s00521-015-2149-8","volume":"28","author":"Z Mohammadi","year":"2017","unstructured":"Mohammadi Z, Frounchi J, Amiri M (2017) Waveletbased emotion recognition system using EEG signal. Neural Comput Appl 28:1985\u20131990. https:\/\/doi.org\/10.1007\/s00521-015-2149-8","journal-title":"Neural Comput Appl"},{"key":"19456_CR8","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. https:\/\/doi.org\/10.1007\/s10772-018-9491-z","journal-title":"Int J Speech Technol"},{"key":"19456_CR9","unstructured":"Li S, Deng W (2018) Deep facial expression recognition: a survey. Computer vision and pattern recognition 1\u201325"},{"key":"19456_CR10","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1007\/s00530-022-00948-0","volume":"28","author":"DP Tob\u00f3n","year":"2022","unstructured":"Tob\u00f3n DP, Hossain MS, Muhammad G et al (2022) Deep learning in multimedia healthcare applications: a review. Multimedia Syst 28:1465\u20131479. https:\/\/doi.org\/10.1007\/s00530-022-00948-0","journal-title":"Multimedia Syst"},{"key":"19456_CR11","unstructured":"ArzoMahmood, UtkuK\u00f6se (2021) Speech recognition based on Convolutional neural networks and MFCC algorithm. Adv Artif Intell Res (AAIR) 1(1):6\u201312"},{"issue":"10","key":"19456_CR12","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"19456_CR13","unstructured":"Transfer Learning and fine-tuning. https:\/\/keras.io\/guides\/transfer_learning\/. Accessed 25 Jun 2023"},{"key":"19456_CR14","doi-asserted-by":"crossref","unstructured":"Pinto MGD,Polignano M, Lopes P, Semeraro G (2020) Emotions Understanding Model from Spoken Language using Deep Neural Networks and Mel-Frequency Cepstral Coefficients. In: EAIS, IEEE, 978\u20131\u20137281\u20134384\u201322020","DOI":"10.1109\/EAIS48028.2020.9122698"},{"issue":"1","key":"19456_CR15","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/TCE.2021.3056421","volume":"67","author":"R Chatterjee","year":"2021","unstructured":"Chatterjee R, Majumder S, Sherratt RS, Halder R, Maitra T, Giri D (2021) Real-time speech emotion analysis for smart home assistants. IEEE Trans Consum Electronics 67(1):68\u201376. https:\/\/doi.org\/10.1109\/TCE.2021.3056421","journal-title":"IEEE Trans Consum Electronics"},{"key":"19456_CR16","doi-asserted-by":"publisher","unstructured":"Iqbal A, Barua K (2019) A real-time emotion recognition from speech using gradient boosting. In: 2019 International Conference on Electrical Computer and Communication Engineering (ECCE), pp 1\u20135. https:\/\/doi.org\/10.1109\/ECACE.2019.8679271","DOI":"10.1109\/ECACE.2019.8679271"},{"key":"19456_CR17","doi-asserted-by":"publisher","unstructured":"Koolagudi SG, Srinivasa Murthy YV, Bhaskar SP (2018) Choice of a classifier, based on properties of a dataset: case study\u2011speech emotion recognition. Int J Speech Technol. https:\/\/doi.org\/10.1007\/s10772-018-9495-8","DOI":"10.1007\/s10772-018-9495-8"},{"key":"19456_CR18","doi-asserted-by":"publisher","unstructured":"Ashar A, Bhatti MS, Mushtaq U (2020) Speaker identification using a hybrid cnn-mfcc approach. In; 2020 International conference on emerging trends in smart technologies (ICETST), pp 1\u20134. https:\/\/doi.org\/10.1109\/ICETST49965.2020.9080730","DOI":"10.1109\/ICETST49965.2020.9080730"},{"key":"19456_CR19","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s10772-023-10047-8","volume":"26","author":"AS Rao","year":"2023","unstructured":"Rao AS, Reddy AP, Vulpala P et al (2023) Deep learning structure for emotion prediction using MFCC from native languages. Int J Speech Technol 26:721\u2013733. https:\/\/doi.org\/10.1007\/s10772-023-10047-8","journal-title":"Int J Speech Technol"},{"key":"19456_CR20","doi-asserted-by":"publisher","unstructured":"Rochlani R, Raut AB (2024) Machine Learning Approach for Detection of Speech Emotions for RAVDESS Audio Dataset. In: 2024 Fourth international conference on advances in electrical, computing, communication and sustainable technologies (ICAECT), Bhilai, India, pp 1\u20137. https:\/\/doi.org\/10.1109\/ICAECT60202.2024.10468810","DOI":"10.1109\/ICAECT60202.2024.10468810"},{"key":"19456_CR21","doi-asserted-by":"publisher","unstructured":"Dolka H, Arul Xavier VM, Juliet S (2021) Speech Emotion Recognition Using ANN on MFCC Features. In: 2021 3rd International Conference on Signal Processing and Communication (ICPSC), Coimbatore, India, pp 431\u2013435. https:\/\/doi.org\/10.1109\/ICSPC51351.2021.9451810","DOI":"10.1109\/ICSPC51351.2021.9451810"},{"key":"19456_CR22","doi-asserted-by":"publisher","unstructured":"Vimal B, Surya M, Darshan, Sridhar VS, Ashok A (2021) MFCC Based Audio Classification Using Machine Learning. In; 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, pp 1\u20134, https:\/\/doi.org\/10.1109\/ICCCNT51525.2021.9579881","DOI":"10.1109\/ICCCNT51525.2021.9579881"},{"key":"19456_CR23","doi-asserted-by":"crossref","unstructured":"Gao Y, Li B, Wang N, Zhu T (2017) Speech emotion recognition using local and global features. In: International Conference on Brain Informatics (pp 3\u201313). Springer, Cham","DOI":"10.1007\/978-3-319-70772-3_1"},{"key":"19456_CR24","doi-asserted-by":"publisher","first-page":"101894","DOI":"10.1016\/j.bspc.2020.101894","volume":"59","author":"D Issa","year":"2020","unstructured":"Issa D, Demirci MF, Yazici A (2020) Speech emotion recognition with deep convolutional neural networks. Biomed Signal Process Control 59:101894","journal-title":"Biomed Signal Process Control"},{"key":"19456_CR25","doi-asserted-by":"publisher","first-page":"79861","DOI":"10.1109\/ACCESS.2020.2990405","volume":"8","author":"M Sajjad","year":"2020","unstructured":"Sajjad M, Kwon S (2020) Clustering-based speech emotion recognition by incorporating learned features and deep BiLSTM. IEEE Access 8:79861\u201379875","journal-title":"IEEE Access"},{"key":"19456_CR26","doi-asserted-by":"crossref","unstructured":"Shegokar P, Sircar P (2016) Continuous wavelet transform based speech emotion recognition. In: 2016 10th International conference on signal processing and communication systems (ICSPCS) (pp 1\u20138). IEEE","DOI":"10.1109\/ICSPCS.2016.7843306"},{"key":"19456_CR27","doi-asserted-by":"publisher","first-page":"104886","DOI":"10.1016\/j.knosys.2019.104886","volume":"184","author":"A Bhavan","year":"2019","unstructured":"Bhavan A, Chauhan P, Shah RR (2019) Bagged support vector machines for emotion recognition from speech. Knowl-Based Syst 184:104886","journal-title":"Knowl-Based Syst"},{"key":"19456_CR28","doi-asserted-by":"crossref","unstructured":"Zhang B, Essl G, Provost EM (2015) Recognizing emotion from singing and speaking using shared models. In: 2015 International conference on affective computing and intelligent interaction (ACII), IEEE, pp 139\u2013145","DOI":"10.1109\/ACII.2015.7344563"},{"key":"19456_CR29","unstructured":"Singh YB, Goel S (2021) 1D CNN based approach for speech emotion recognition using MFCC features. Artificial Intelligence and Speech Technology, Taylor & Francis, ISBN, 9781003150664"},{"key":"19456_CR30","doi-asserted-by":"publisher","unstructured":"Hazra SK, Ema RR, Galib SMD, Kabir S, Adnan N (2022) Emotion recognition of human speech using deep learning method and mfcc features. Radio Electron Comput Syst 4(104). https:\/\/doi.org\/10.32620\/reks.2022.4.13","DOI":"10.32620\/reks.2022.4.13"},{"key":"19456_CR31","doi-asserted-by":"publisher","first-page":"4376","DOI":"10.3390\/electronics12204376","volume":"12","author":"K Mountzouris","year":"2023","unstructured":"Mountzouris K, Perikos I, Hatzilygeroudis I (2023) Speech Emotion Recognition Using Convolutional Neural Networks with Attention Mechanism. Electronics 12:4376. https:\/\/doi.org\/10.3390\/electronics12204376","journal-title":"Electronics"},{"key":"19456_CR32","doi-asserted-by":"publisher","first-page":"183","DOI":"10.3390\/s20010183","volume":"20","author":"MS Kwon","year":"2019","unstructured":"Kwon MS (2019) A CNN-assisted enhanced audio signal processing for speech emotion recognition. Sensors 20:183. https:\/\/doi.org\/10.3390\/s20010183","journal-title":"Sensors"},{"key":"19456_CR33","doi-asserted-by":"publisher","first-page":"20200363","DOI":"10.1098\/rsta.2020.0363","volume":"379","author":"JA McDermid","year":"2021","unstructured":"McDermid JA, Jia Y, Porter Z, Habli I (2021) Artificial intelligence explainability: the technical and ethical dimensions. Phil Trans R Soc A 379:20200363. https:\/\/doi.org\/10.1098\/rsta.2020.0363","journal-title":"Phil Trans R Soc A"},{"key":"19456_CR34","unstructured":"Hore S, Banerjee S, Bhattacharya T (2022) A smart system for assessment of mental health using explainable AI Approach. In: Proceedings of the 7th international conference on emerging applications of information technology (EAIT 2022), Springer"},{"key":"19456_CR35","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/s42452-021-04148-9","volume":"3","author":"M Saarela","year":"2021","unstructured":"Saarela M, Jauhiainen S (2021) Comparison of feature importance measures as explanations for classification models. SN Appl Sci 3:272. https:\/\/doi.org\/10.1007\/s42452-021-04148-9","journal-title":"SN Appl Sci"},{"issue":"177","key":"19456_CR36","first-page":"1","volume":"20","author":"A Fisher","year":"2019","unstructured":"Fisher A, Rudin C, Dominici F (2019) All models are wrong, but many are useful: Learning a variable\u2019s importance by studying an entire class of prediction models simultaneously. J Mach Learn Res 20(177):1\u201381","journal-title":"J Mach Learn Res"},{"issue":"3","key":"19456_CR37","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1177\/1461444816676645","volume":"20","author":"M Ananny","year":"2018","unstructured":"Ananny M, Crawford K (2018) Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media Soc 20(3):973\u2013989","journal-title":"New Media Soc"},{"key":"19456_CR38","doi-asserted-by":"crossref","unstructured":"Diakopoulos N (2017) Enabling accountability of algorithmic media: transparency as a constructive and critical lens. In: Transparent data mining for big and small data. Springer 25\u201343","DOI":"10.1007\/978-3-319-54024-5_2"},{"key":"19456_CR39","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.procs.2020.01.079","volume":"165","author":"S Velliangiria","year":"2019","unstructured":"Velliangiria S, Alagumuthukrishnan S, Iwin S, Joseph T (2019) A review of dimensionality reduction techniques for efficient computation. Procedia Comput Sci 165:104\u2013111. https:\/\/doi.org\/10.1016\/j.procs.2020.01.079","journal-title":"Procedia Comput Sci"},{"key":"19456_CR40","doi-asserted-by":"publisher","first-page":"4086","DOI":"10.3390\/electronics11244086","volume":"11","author":"P Guleria","year":"2022","unstructured":"Guleria P, Naga Srinivasu P, Ahmed S, Almusallam N, Alarfaj FK (2022) XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques. Electron 11:4086. https:\/\/doi.org\/10.3390\/electronics11244086","journal-title":"Electron"},{"key":"19456_CR41","doi-asserted-by":"publisher","unstructured":"Naga Srinivasu P, Sandhya N, Jhaveri RH, Rau R (2022) From blackbox to explainable AI in healthcare: existing tools and case studies Hindaw. iMobile Information Systems 2022, Article ID 8167821, 20. https:\/\/doi.org\/10.1155\/2022\/8167821","DOI":"10.1155\/2022\/8167821"},{"key":"19456_CR42","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1007\/s00530-022-00957-z","volume":"28","author":"M Jagadeesh","year":"2022","unstructured":"Jagadeesh M, Baranidharan B (2022) Facial expression recognition of online learners from real-time videos using a novel deep learning model. Multimedia Syst 28:2285\u20132305. https:\/\/doi.org\/10.1007\/s00530-022-00957-z","journal-title":"Multimedia Syst"},{"key":"19456_CR43","doi-asserted-by":"crossref","unstructured":"Reddy B, Kim Y-H, Yun S, Jang J, Hong S (2016) End to end deep learning for single step real-time facial expression recognition, video analytics. Face and Facial Expression Recogn. Audience Measurement 10165:88\u201397","DOI":"10.1007\/978-3-319-56687-0_8"},{"key":"19456_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-022-00986-8","author":"Z Lin","year":"2022","unstructured":"Lin Z, She J, Shen Q (2022) Real emotion seeker: recalibrating annotation for facial expression recognition. Multimedia Syst. https:\/\/doi.org\/10.1007\/s00530-022-00986-8","journal-title":"Multimedia Syst"},{"key":"19456_CR45","unstructured":"Model used for Facial Emotion Recognition. https:\/\/github.com\/serengil\/deepface"},{"key":"19456_CR46","first-page":"10","volume":"9","author":"M Mukeshimana","year":"2017","unstructured":"Mukeshimana M, Ban X, Karani N, Liu R (2017) Multimodal emotion recognition for human-computer interaction: A survey. System 9:10","journal-title":"System"},{"key":"19456_CR47","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.inffus.2017.02.003","volume":"37","author":"S Poria","year":"2017","unstructured":"Poria S, Cambria E, Bajpai R, Hussain A (2017) A review 522 of affective computing: from unimodal analysis to multimodal fusion. Inform Fusion 37:98\u2013125. https:\/\/doi.org\/10.1016\/j.inffus.2017.02.003","journal-title":"Inform Fusion"},{"key":"19456_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-022-01018-1","author":"Z Shanqing","year":"2022","unstructured":"Shanqing Z, Yujie C, Yiheng M et al (2022) A multi-level feature weight fusion model for salient object detection. Multimedia Syst. https:\/\/doi.org\/10.1007\/s00530-022-01018-1","journal-title":"Multimedia Syst"},{"issue":"5","key":"19456_CR49","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1109\/TMM.2008.927665","volume":"10","author":"Y Wang","year":"2008","unstructured":"Wang Y, Guan L (2008) Recognizing human emotional state from audiovisual signals. IEEE Trans Multimed 10(5):936\u2013946","journal-title":"IEEE Trans Multimed"},{"key":"19456_CR50","doi-asserted-by":"crossref","unstructured":"Busso, Deng Z, Yildirim S, Bulut M, Lee CM, Kazemzadeh A, Lee S, Neumann U, Narayanan S (2004) Analysis of emotion recognition using facial expressions, speech and multimodal information. In: Proceedings of the 6th international conference on multimodal interfaces, pp 205\u2013211","DOI":"10.1145\/1027933.1027968"},{"issue":"7","key":"19456_CR51","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1109\/TMM.2016.2557721","volume":"18","author":"J Yan","year":"2016","unstructured":"Yan J, Zheng W, Xu Q, Lu G, Li H, Wang B (2016) Sparse kernel reduced rank regression for bimodal emotion recognition from facial expression and speech. IEEE Trans Multimed 18(7):1319\u20131329","journal-title":"IEEE Trans Multimed"},{"key":"19456_CR52","doi-asserted-by":"publisher","first-page":"20200423","DOI":"10.1007\/s11042-023-14543-6","volume":"10","author":"S Hore","year":"2023","unstructured":"Hore S, Bhattacharya T (2023) Impact of Lockdown on Generation-Z: A Fuzzy based Multimodal Emotion Recognition Approach using CNN. MultiMed Tools Appl (MTAP) 10:20200423. https:\/\/doi.org\/10.1007\/s11042-023-14543-6","journal-title":"MultiMed Tools Appl (MTAP)"},{"key":"19456_CR53","doi-asserted-by":"crossref","unstructured":"Xu F, Wang Z (2018) Emotion recognition research based on integration of facial expression and voice. In: 2018 11th International congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), IEEE, pp 1\u20136","DOI":"10.1109\/CISP-BMEI.2018.8633129"},{"key":"19456_CR54","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.jvcir.2019.03.002","volume":"60","author":"KP Rao","year":"2019","unstructured":"Rao KP, Rao MCS, Chowdary NH (2019) An integrated approach to emotion recognition and gender classification. J Vis Commun Image Represent 60:339\u2013345","journal-title":"J Vis Commun Image Represent"},{"key":"19456_CR55","doi-asserted-by":"crossref","unstructured":"Miao H, Zhang Y, Li W, Zhang H, Wang D, Feng S (2018) Chinese multimodal emotion recognition in deep and traditional machine leaming approaches, In: 2018 First asian conference on affective computing and intelligent interaction (ACII Asia), IEEE, pp 1\u20136","DOI":"10.1109\/ACIIAsia.2018.8470379"},{"issue":"5","key":"19456_CR56","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1007\/s00138-018-0960-9","volume":"30","author":"E Avots","year":"2019","unstructured":"Avots E, Sapi\u0144ski T, Bachmann M, Kami\u0144ska D (2019) Audiovisual emotion recognition in wild. Mach Vis Appl 30(5):975\u2013985","journal-title":"Mach Vis Appl"},{"key":"19456_CR57","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.cviu.2015.09.015","volume":"147","author":"X Huang","year":"2016","unstructured":"Huang X, Kortelainen J, Zhao G, Li X, Moilanen A, Sepp\u00e4nen T, Pietik\u00e4inen M (2016) Multi-modal emotion analysis from facial expressions and electroencephalogram. Comput Vis Image Underst 147:114\u2013124","journal-title":"Comput Vis Image Underst"},{"issue":"5","key":"19456_CR58","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1109\/MIS.2020.2992799","volume":"35","author":"Y Susanto","year":"2020","unstructured":"Susanto Y, Livingstone AG, Ng BC, Cambria E (2020) The hourglass model revisited. IEEE Intell Syst 35(5):96\u2013102","journal-title":"IEEE Intell Syst"},{"issue":"2","key":"19456_CR59","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/mis.2021.3062200","volume":"36","author":"L Stappen","year":"2021","unstructured":"Stappen L, Baird A, Cambria E, Schuller BW, Cambria E (2021) Sentiment analysis and topic recognition in video transcriptions. IEEE Intell Syst 36(2):88\u201395. https:\/\/doi.org\/10.1109\/mis.2021.3062200","journal-title":"IEEE Intell Syst"},{"key":"19456_CR60","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2021.3072412","author":"K Zhang","year":"2021","unstructured":"Zhang K, Li Y, Wang J, Cambria E, Li X (2021) Real-time video emotion recognition based on reinforcement learning and domain knowledge. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/tcsvt.2021.3072412","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"19456_CR61","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1007\/s00530-021-00800-x","volume":"28","author":"K Shankar","year":"2022","unstructured":"Shankar K, Perumal E, Tiwari P et al (2022) Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images. Multimedia Syst 28:1175\u20131187. https:\/\/doi.org\/10.1007\/s00530-021-00800-x","journal-title":"Multimedia Syst"},{"key":"19456_CR62","doi-asserted-by":"crossref","unstructured":"Yoshitomi Y, Kim S-I, Kawano T, Kilazoe T (2000) Effect of sensor fusion for recognition of emotional states using voice, face image and thermal image of face. In: Proceedings 9th IEEE International workshop on robot and human interactive communication. IEEE RO-MAN 2000 (Cat. No. 00TH8499), IEEE, pp 178\u2013183","DOI":"10.1109\/ROMAN.2000.892491"},{"key":"19456_CR63","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.cviu.2018.06.005","volume":"174","author":"D Nguyen","year":"2018","unstructured":"Nguyen D, Nguyen K, Sridharan S, Dean D, Fookes C (2018) Deep spatio temporal feature fusion with compact bilinear pooling for multimodal emotion recognition. Comput Vis Image Underst 174:33\u201342","journal-title":"Comput Vis Image Underst"},{"issue":"3","key":"19456_CR64","doi-asserted-by":"publisher","first-page":"108580","DOI":"10.1016\/j.knosys.2022.108580","volume":"244","author":"SI Middya","year":"2022","unstructured":"Middya SI, Nag B, Roy S (2022) Deep learning based multimodal emotion recognition using model-level fusion of audio-visual modalities. Knowledge-Based Syst 244(3):108580. https:\/\/doi.org\/10.1016\/j.knosys.2022.108580","journal-title":"Knowledge-Based Syst"},{"key":"19456_CR65","unstructured":"Mitchell M, NatCen CH (2009) Trans research review, Equality, and Human Rights Commission Research report 27, \u00a9 Equality and Human Rights Commission 2009 First published Autumn, ISBN 9781842061602"},{"key":"19456_CR66","unstructured":"Pamuela H (2019) The psychological & emotional effects of discrimination within the LGBTQ, transgender, & non-binary communities. Thomas Jefferson Law Review 41(2)"},{"issue":"1","key":"19456_CR67","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1002\/j.1556-6678.2011.tb00057.x","volume":"89","author":"AA Singh","year":"2011","unstructured":"Singh AA, Hays DG, Watson LS (2011) Strength in the face of adversity: resilience strategies of transgender individuals. J Couns Dev 89(1):20\u201327","journal-title":"J Couns Dev"},{"issue":"2","key":"19456_CR68","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1177\/1534765610369261","volume":"17","author":"AA Singh","year":"2011","unstructured":"Singh AA, McKleroy VS (2011) \u201cJust getting out of bed is a revolutionary act\u201d: the resilience of transgender people of color who have survived traumatic life events. Traumatology. 17(2):34\u201344","journal-title":"Traumatology."},{"issue":"2","key":"19456_CR69","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1002\/j.1556-6676.2014.00150.x","volume":"92","author":"AA Singh","year":"2014","unstructured":"Singh AA, Meng SE, Hansen AW (2014) \u201cI am my own gender\u201d: resilience strategies of Tran\u2019s youth. J Couns Dev. 92(2):208\u2013218","journal-title":"J Couns Dev."},{"key":"19456_CR70","doi-asserted-by":"publisher","unstructured":"Bariola E, Lyons A, Leonard W, Pitts M, Badcock P, Couch M (2015) Demographic and Psychosocial Factors Associated With Psychological Distress and Resilience Among Transgender Individuals. Am J Public Health. 105(10): 2108\u20132116. Published online 2015 October.\u00a0https:\/\/doi.org\/10.2105\/AJPH.2015.302763","DOI":"10.2105\/AJPH.2015.302763"},{"issue":"3","key":"19456_CR71","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1177\/0011000014565715","volume":"43","author":"SL Budge","year":"2015","unstructured":"Budge SL, Orovecz JJ, Thai JL (2015) Trans men\u2019s positive emotions: the interaction of gender identity and emotion labels. Couns Psychol 43(3):404\u2013434. https:\/\/doi.org\/10.1177\/0011000014565715","journal-title":"Couns Psychol"},{"key":"19456_CR72","doi-asserted-by":"publisher","unstructured":"Sell IM (2008) Third Gender: A Qualitative Study of the Experience of Individuals Who Identify as Being Neither Man nor Woman, published online: 20 Oct 2008, Pages 131-145.https:\/\/doi.org\/10.1300\/J358v13n01_06","DOI":"10.1300\/J358v13n01_06"},{"key":"19456_CR73","doi-asserted-by":"publisher","unstructured":"Mueller SC, De Cuypere G, T\u2019Sjoen G (2017) Transgender research in the 21st century: a selective critical review from a neurocognitive perspective, Published.https:\/\/doi.org\/10.1176\/appi.ajp.2017.17060626","DOI":"10.1176\/appi.ajp.2017.17060626"},{"issue":"2","key":"19456_CR74","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1080\/19359705.2011.553779","volume":"15","author":"S Sennott","year":"2011","unstructured":"Sennott S, Smith T (2011) Translating the sex and gender continuums in mental health: A transfeminist approach to client and clinician fears. J Gay Lesbian Mental Health 15(2):218\u2013234. https:\/\/doi.org\/10.1080\/19359705.2011.553779","journal-title":"J Gay Lesbian Mental Health"},{"key":"19456_CR75","doi-asserted-by":"publisher","unstructured":"Marshall Z, Welch V, Minichiello A, Swab M, Brunger F, Kaposy C (2019) Documenting research with transgender, nonbinary, and other gender diverse (Trans) individuals and communities: introducing the global trans research evidence map. Transgender Health 4.1. https:\/\/doi.org\/10.1089\/trgh.2018.0020","DOI":"10.1089\/trgh.2018.0020"},{"key":"19456_CR76","doi-asserted-by":"publisher","unstructured":"Refaeilzadeh P, Tang L, Liu H (2009) Cross-Validation. In: LIU L, \u00d6zsu MT (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https:\/\/doi.org\/10.1007\/978-0-387-39940-9_565","DOI":"10.1007\/978-0-387-39940-9_565"},{"key":"19456_CR77","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.procs.2021.08.128","volume":"192","author":"G Baron","year":"2021","unstructured":"Baron G, Sta\u0144czyk U (2021) Standard vs. non-standard cross-validation: evaluation of performance in a space with structured distribution of datapoints. Procedia Comput Sci 192:1245\u20131254. https:\/\/doi.org\/10.1016\/j.procs.2021.08.128","journal-title":"Procedia Comput Sci"},{"key":"19456_CR78","doi-asserted-by":"publisher","unstructured":"Data source: 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. https:\/\/doi.org\/10.1371\/journal.pone.0196391","DOI":"10.1371\/journal.pone.0196391"},{"key":"19456_CR79","unstructured":"Python library used for trimming speech clips. https:\/\/pypi.org\/project\/pyvad\/"},{"issue":"11","key":"19456_CR80","doi-asserted-by":"publisher","first-page":"2739","DOI":"10.3390\/diagnostics12112739","volume":"12","author":"S Ahmed","year":"2022","unstructured":"Ahmed S, Naga Srinivasu P, Alhumam A, Alarfa MD (2022) AAL and internet of medical things for monitoring type-2 diabetic patients. Diagnostics (Basel). 12(11):2739. https:\/\/doi.org\/10.3390\/diagnostics12112739","journal-title":"Diagnostics (Basel)."},{"key":"19456_CR81","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/app13010014","volume":"13","author":"C Maraveas","year":"2023","unstructured":"Maraveas C (2023) Incorporating artificial intelligence technology in smart greenhouses: Current State of the Art. Appl Sci 13:14","journal-title":"Appl Sci"},{"key":"19456_CR82","doi-asserted-by":"publisher","first-page":"106993","DOI":"10.1016\/j.compag.2022.106993","volume":"198","author":"C Maraveas","year":"2022","unstructured":"Maraveas C, Piromalis D, Arvanitis KG, Bartzanas T, Loukatos D (2022) Applications of IoT for optimized greenhouse environment and resources management. Comput Electron Agric 198:106993","journal-title":"Comput Electron Agric"},{"issue":"Suppl 2","key":"19456_CR83","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10516-021-09610-2","volume":"32","author":"V Matarese","year":"2022","unstructured":"Matarese V (2022) Kinds of replicability: different terms and different functions. Axiomathes 32(Suppl 2):647\u2013670. https:\/\/doi.org\/10.1007\/s10516-021-09610-2","journal-title":"Axiomathes"},{"key":"19456_CR84","doi-asserted-by":"publisher","unstructured":"Baker M (2020) Why scientists must share their research code. Nature. https:\/\/doi.org\/10.1038\/nature.2016.20504","DOI":"10.1038\/nature.2016.20504"},{"key":"19456_CR85","unstructured":"Video Data Source:(Trans People Speak video series). https:\/\/glaad.org\/transpeoplespeak\/"},{"key":"19456_CR86","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1109\/89.294354","volume":"2","author":"J-C Junqua","year":"1994","unstructured":"Junqua J-C, Mak B, Reaves B (1994) A robust algorithm for word boundary detection in presence of noise. IEEE Trans on Speech Audio Process 2:406\u2013412","journal-title":"IEEE Trans on Speech Audio Process"},{"key":"19456_CR87","unstructured":"Meduri SS, Ananth R (2012) A survey and evaluation of voice activity detection algorithms. Lambert Academic Publishing"},{"key":"19456_CR88","doi-asserted-by":"crossref","unstructured":"Bachu RG, Kopparthi S, Adapa B, Barkana BD (2010) Voiced\/unvoiced decision for speech signals based on zero-crossing rate and energy","DOI":"10.1007\/978-90-481-3660-5_47"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19456-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-19456-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-19456-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T11:24:34Z","timestamp":1747999474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-19456-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,17]]},"references-count":88,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["19456"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-19456-6","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,17]]},"assertion":[{"value":"21 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}