{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T14:35:11Z","timestamp":1758119711293,"version":"3.44.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"31","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"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\/s11042-025-20718-0","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T04:37:20Z","timestamp":1741322240000},"page":"38545-38572","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel approach to deriving adaboost classifier weights using squared loss function for overlapping speech detection"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3587-291X","authenticated-orcid":false,"given":"Nassim","family":"Asbai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hadjer","family":"Bounazou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sihem","family":"Zitouni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"20718_CR1","doi-asserted-by":"crossref","unstructured":"Cornell S, Omologo M, Squartini S, Vincent E (2020) Detecting and counting overlapping speakers in distant speech scenarios. In INTERSPEECH 2020","DOI":"10.21437\/Interspeech.2020-2671"},{"key":"20718_CR2","doi-asserted-by":"crossref","unstructured":"Jarashanth S T, Ahilan K, Valluvan R, Thiruvaran T, Kaneswaran A (2022) Overlapped Speech Detection for Improved Speaker Diarization on Tamil Dataset. In 2022 6th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI) (pp. 1\u20135). IEEE","DOI":"10.1109\/SLAAI-ICAI56923.2022.10002438"},{"key":"20718_CR3","doi-asserted-by":"crossref","unstructured":"Raj D, Denisov P, Chen Z, Erdogan H, Huang Z, He M, Hershey J R (2021) Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis. In 2021 IEEE spoken language technology workshop (SLT) (pp. 897\u2013904). IEEE","DOI":"10.1109\/SLT48900.2021.9383556"},{"issue":"12","key":"20718_CR4","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/TASLP.2014.2346315","volume":"22","author":"SH Yella","year":"2014","unstructured":"Yella SH, Bourlard H (2014) Overlapping speech detection using long-term conversational features for speaker diarization in meeting room conversations. IEEE\/ACM Trans Audio Speech Lang Process 22(12):1688\u20131700","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process"},{"key":"20718_CR5","doi-asserted-by":"publisher","first-page":"107801","DOI":"10.1016\/j.compag.2023.107801","volume":"209","author":"Z Sun","year":"2023","unstructured":"Sun Z, Gao M, Zhang M, Lv M, Wang G (2023) Research on recognition method of broiler overlapping sounds based on random forest and confidence interval. Comput Electron Agric 209:107801","journal-title":"Comput Electron Agric"},{"key":"20718_CR6","first-page":"1","volume":"2020","author":"C Liu","year":"2020","unstructured":"Liu C, Ren Y, Liang M, Gu Z, Wang J, Pan L, Wang Z (2020) Detecting overlapping data in system logs based on ensemble learning method. Wirel Commun Mob Comput 2020:1\u20138","journal-title":"Wirel Commun Mob Comput"},{"key":"20718_CR7","doi-asserted-by":"crossref","unstructured":"Bahad P, Saxena P (2020) Study of adaboost and gradient boosting algorithms for predictive analytics. In International Conference on Intelligent Computing and Smart Communication 2019: Proceedings of ICSC 2019 (pp. 235\u2013244). Springer Singapore","DOI":"10.1007\/978-981-15-0633-8_22"},{"issue":"6","key":"20718_CR8","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.1109\/TSMCC.2012.2227471","volume":"42","author":"Y Gao","year":"2012","unstructured":"Gao Y, Ji G, Yang Z, Pan J (2012) A dynamic AdaBoost algorithm with adaptive changes of loss function. IEEE Trans Syst Man Cybern C (Appl Rev) 42(6):1828\u20131841","journal-title":"IEEE Trans Syst Man Cybern C (Appl Rev)"},{"key":"20718_CR9","unstructured":"Neves de Souza E (2014) Extending adaboost: varying the base learners and modifying the weight calculation (Doctoral dissertation, Universit\u00e9 d'Ottawa\/University of Ottawa)"},{"key":"20718_CR10","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.knosys.2014.08.003","volume":"71","author":"K Wang","year":"2014","unstructured":"Wang K, Zhong P (2014) Robust non-convex least squares loss function for regression with outliers. Knowl-Based Syst 71:290\u2013302","journal-title":"Knowl-Based Syst"},{"key":"20718_CR11","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3389\/fnbot.2013.00021","volume":"7","author":"A Natekin","year":"2013","unstructured":"Natekin A, Knoll A (2013) Gradient boosting machines, a tutorial. Front Neurorobot 7:21","journal-title":"Front Neurorobot"},{"issue":"263","key":"20718_CR12","first-page":"1","volume":"22","author":"C Christof","year":"2021","unstructured":"Christof C (2021) On the stability properties and the optimization landscape of training problems with squared loss for neural networks and general nonlinear conic approximation schemes. J Mach Learn Res 22(263):1\u201377","journal-title":"J Mach Learn Res"},{"key":"20718_CR13","unstructured":"Kunapuli, G. (2023). Ensemble Methods for Machine Learning. Manning Publications."},{"key":"20718_CR14","doi-asserted-by":"publisher","first-page":"116988","DOI":"10.1016\/j.engstruct.2023.116988","volume":"297","author":"Q Du Nguyen","year":"2023","unstructured":"Du Nguyen Q, Thai HT (2023) Crack segmentation of imbalanced data: The role of loss functions. Eng Struct 297:116988","journal-title":"Eng Struct"},{"key":"20718_CR15","doi-asserted-by":"publisher","first-page":"125054","DOI":"10.1109\/ACCESS.2019.2938356","volume":"7","author":"YS Jeon","year":"2019","unstructured":"Jeon YS, Yang DH, Lim DJ (2019) FlexBoost: A flexible boosting algorithm with adaptive loss functions. IEEE Access 7:125054\u2013125061","journal-title":"IEEE Access"},{"key":"20718_CR16","doi-asserted-by":"crossref","unstructured":"Kobayashi D, Kajita S, Takeda K, Itakura F (1996) Extracting speech features from human speech-like noise. Proceeding of 4th International Conference on Spoken Language Processing. Vol. 1, 418\u2013421","DOI":"10.21437\/ICSLP.1996-88"},{"key":"20718_CR17","doi-asserted-by":"publisher","unstructured":"Boakye K, Vinyals O, Friedland G (2011) Improved overlapped speech handling for speaker diarization. 12th Annual Conference of the International Speech Communication Association, 941\u2013944. https:\/\/doi.org\/10.21437\/Interspeech.2011-382","DOI":"10.21437\/Interspeech.2011-382"},{"key":"20718_CR18","doi-asserted-by":"crossref","unstructured":"Zelen\u00e1k M, Hernando J (2011) The detection of overlapping speech with prosodic features for speaker diarization. 12th Annual Conference of the International Speech Communication Association, 1041\u20131044","DOI":"10.21437\/Interspeech.2011-389"},{"key":"20718_CR19","doi-asserted-by":"publisher","unstructured":"Snyder D, Garcia-Romero D, Povey D, Khudanpur S (2017) Deep neural network embeddings for text-independent speaker verification. 18th Annual Conference of the International Speech Communication Association, 999\u20131003.https:\/\/doi.org\/10.21437\/Interspeech.2017-620","DOI":"10.21437\/Interspeech.2017-620"},{"issue":"1","key":"20718_CR20","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/TSA.2004.838531","volume":"13","author":"SN Wrigley","year":"2005","unstructured":"Wrigley SN, Brown GJ, Wan V, Renals S (2005) Speech and crosstalk detection in multichannel audio. IEEE Trans Speech Audio Process 13(1):84\u201391","journal-title":"IEEE Trans Speech Audio Process"},{"issue":"23","key":"20718_CR21","doi-asserted-by":"publisher","first-page":"29394","DOI":"10.1109\/JSEN.2023.3327297","volume":"23","author":"Y Guan","year":"2023","unstructured":"Guan Y, Xu H, Liu W, Li C, Liu Y (2023) A False-alarm-controllable Modified AdaBoost Wake Detection Method Using SAR Images. IEEE Sens J 23(23):29394\u201329405","journal-title":"IEEE Sens J"},{"issue":"1","key":"20718_CR22","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.specom.2009.08.009","volume":"52","author":"T Kinnunen","year":"2010","unstructured":"Kinnunen T, Li H (2010) An overview of text-independent speaker recognition: From features to supervectors. Speech Commun 52(1):12\u201340","journal-title":"Speech Commun"},{"key":"20718_CR23","doi-asserted-by":"publisher","first-page":"101483","DOI":"10.1016\/j.csl.2023.101483","volume":"81","author":"S Maruf","year":"2023","unstructured":"Maruf S, Zukerman I, Reiter E, Haffari G (2023) Influence of context on users\u2019 views about explanations for decision-tree predictions. Comput Speech Lang 81:101483","journal-title":"Comput Speech Lang"},{"key":"20718_CR24","doi-asserted-by":"publisher","first-page":"170209","DOI":"10.1016\/j.scitotenv.2024.170209","volume":"916","author":"AK Asri","year":"2024","unstructured":"Asri AK, Lee HY, Chen YL, Wong PY, Hsu CY, Chen PC, Wu CD (2024) A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan. Sci Total Environ 916:170209","journal-title":"Sci Total Environ"},{"key":"20718_CR25","doi-asserted-by":"publisher","unstructured":"Mahkamov A A, Jumayev T S, Tuhtanazarov D S, Dadamuxamedov A I (2024) Using AdaBoost to improve the performance of simple classifiers. In\u00a0Artificial Intelligence, Blockchain, Computing and Security Volume 2\u00a0(pp. 755\u2013760). CRC Press. https:\/\/doi.org\/10.1201\/9781032684994-122","DOI":"10.1201\/9781032684994-122"},{"key":"20718_CR26","doi-asserted-by":"publisher","first-page":"120200","DOI":"10.1016\/j.eswa.2023.120200","volume":"227","author":"AN Tabata","year":"2023","unstructured":"Tabata AN, Zimmer A, dos Santos CL, Mariani VC (2023) Analyzing CARLA\u2019s performance for 2D object detection and monocular depth estimation based on deep learning approaches. Expert Syst Appl 227:120200","journal-title":"Expert Syst Appl"},{"key":"20718_CR27","doi-asserted-by":"publisher","first-page":"106670","DOI":"10.1016\/j.bspc.2024.106670","volume":"97","author":"ASC Bose","year":"2024","unstructured":"Bose ASC, Srinivasan C, Immaculate Joy S (2024) Optimized feature selection for enhanced accuracy in knee osteoarthritis detection and severity classification with machine learning. Biomed Signal Process Control 97:106670","journal-title":"Biomed Signal Process Control"},{"key":"20718_CR28","unstructured":"NIST Multimodal Information Group (2011) 2005 NIST Speaker Recognition Evaluation Training SData LDC2011S01. Web Download. Philadelphia: Linguistic Data Consortium"},{"key":"20718_CR29","doi-asserted-by":"publisher","first-page":"122136","DOI":"10.1016\/j.eswa.2023.122136","volume":"238","author":"B Cheng","year":"2024","unstructured":"Cheng B, Liu Y, Jia Y (2024) Evaluation of students\u2019 performance during the academic period using the XG-Boost classifier-enhanced AEO hybrid model. Expert Syst Appl 238:122136","journal-title":"Expert Syst Appl"},{"key":"20718_CR30","first-page":"364","volume":"5","author":"C Zhao","year":"2024","unstructured":"Zhao C, Yan Z, Sun X, Wu M (2024) Enhancing aspect category detection in imbalanced online reviews: An integrated approach using Select-SMOTE and LightGBM. Int J Intell Netw 5:364\u2013372","journal-title":"Int J Intell Netw"},{"issue":"3","key":"20718_CR31","doi-asserted-by":"publisher","first-page":"20240027","DOI":"10.57197\/JDR-2024-0027","volume":"3","author":"AK Dutta","year":"2024","unstructured":"Dutta AK, WahabSait AR (2024) A fine-tuned catboost-based speech disorder detection model. J Disabil Res 3(3):20240027","journal-title":"J Disabil Res"},{"key":"20718_CR32","doi-asserted-by":"publisher","first-page":"83963","DOI":"10.1007\/s11042-024-19017-x","volume":"83","author":"AI Middya","year":"2024","unstructured":"Middya AI, Nag B, Roy S (2024) Effective MLP and CNN based ensemble learning for speech emotion recognition. Multimedia Tools Appl 83:83963\u201383990","journal-title":"Multimedia Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20718-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20718-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20718-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T09:42:55Z","timestamp":1758102175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20718-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,7]]},"references-count":32,"journal-issue":{"issue":"31","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["20718"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20718-0","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2025,3,7]]},"assertion":[{"value":"7 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}