{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T08:38:52Z","timestamp":1777711132705,"version":"3.51.4"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"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\/s11042-023-16748-1","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T13:01:43Z","timestamp":1694782903000},"page":"30145-30166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Hybrid deep learning based automatic speech recognition model for recognizing non-Indian languages"],"prefix":"10.1007","volume":"83","author":[{"given":"Astha","family":"Gupta","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2659-5941","authenticated-orcid":false,"given":"Rakesh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yogesh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"16748_CR1","doi-asserted-by":"publisher","unstructured":"Bhable S, Kayte C (2020) Review: Multilingual Acoustic modeling of Automatic Speech Recognition (ASR) for low resource languages. In IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI).https:\/\/doi.org\/10.1109\/ICATMRI51801.2020.9398431","DOI":"10.1109\/ICATMRI51801.2020.9398431"},{"key":"16748_CR2","doi-asserted-by":"publisher","unstructured":"Malik M, Malik K, Mehmood K, Makhdoom I (2021) Automatic speech recognition: a survey. In Multimedia Tools and Applications, 9411\u20139457. https:\/\/doi.org\/10.1007\/s11042-020-10073-7.","DOI":"10.1007\/s11042-020-10073-7"},{"key":"16748_CR3","doi-asserted-by":"publisher","unstructured":"Xiaohui Chu X (2021) Speech Recognition Method Based on Deep Learning and Its Application. In IEEE International Conference of Social Computing and Digital Economy (ICSCDE). https:\/\/doi.org\/10.1109\/ICSCDE54196.2021.00075","DOI":"10.1109\/ICSCDE54196.2021.00075"},{"key":"16748_CR4","doi-asserted-by":"publisher","first-page":"8127","DOI":"10.1007\/s11042-020-10119-w","volume":"80","author":"E Kalhor","year":"2021","unstructured":"Kalhor E, Bakhtiari B (2021) Speaker independent feature selection for speech emotion recognition: A multi-task approach. In Multimedia Tools and Applications 80:8127\u20138146. https:\/\/doi.org\/10.1007\/s11042-020-10119-w","journal-title":"In Multimedia Tools and Applications"},{"key":"16748_CR5","doi-asserted-by":"publisher","unstructured":"Guntur R, Ramakrishnan K, Mittal V (2021) Automatic Classification of Foreign Language Accent. In IEEE 2nd Global Conference for Advancement in Technology (GCAT). https:\/\/doi.org\/10.1109\/GCAT52182.2021.9587650","DOI":"10.1109\/GCAT52182.2021.9587650"},{"key":"16748_CR6","doi-asserted-by":"publisher","first-page":"9969","DOI":"10.1007\/s11042-022-12304-5","volume":"81","author":"Y Dokuz","year":"2022","unstructured":"Dokuz Y, Tufekci Z (2022) Feature-based hybrid strategies for gradient descent optimization in end-to-end speech recognition. In Multimedia Tools Appl 81:9969\u20139988. https:\/\/doi.org\/10.1007\/s11042-022-12304-5","journal-title":"In Multimedia Tools Appl"},{"key":"16748_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/4368036","volume":"2019","author":"V Delic","year":"2019","unstructured":"Delic V, Peric Z, Secujski M, Jakovljevic N, Nikolic J, Miskovic D, Simic N, Suzic S, Delic T (2019) Speech Technology Progress Based on New Machine Learning Paradigm. Hindawi: Comput Intell Neurosci 2019:1\u201319. https:\/\/doi.org\/10.1155\/2019\/4368036","journal-title":"Hindawi: Comput Intell Neurosci"},{"key":"16748_CR8","doi-asserted-by":"publisher","unstructured":"Abushariah A, Ting H, Mustafa M, Khairuddin A, Abushariah M, Tan T (2022) Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges. In IEEE Access, 5944\u20135954. https:\/\/doi.org\/10.1109\/ACCESS.2022.3218684","DOI":"10.1109\/ACCESS.2022.3218684"},{"key":"16748_CR9","doi-asserted-by":"publisher","first-page":"32593","DOI":"10.1007\/s11042-022-13054-0","volume":"81","author":"I Thukroo","year":"2022","unstructured":"Thukroo I, Bashir R, Giri K (2022) A review into deep learning techniques for spoken language identification. Multimedia Tools Appl 81:32593\u201332624. https:\/\/doi.org\/10.1007\/s11042-022-13054-0","journal-title":"Multimedia Tools Appl"},{"key":"16748_CR10","doi-asserted-by":"publisher","unstructured":"Xue Y, Gao S, Sun H, Qin W (2017) A Chinese Sign Language Recognition System Using Leap Motion. In International Conference on Virtual Reality and Visualization, 180\u2013185. https:\/\/doi.org\/10.1109\/ICVRV.2017.00044","DOI":"10.1109\/ICVRV.2017.00044"},{"key":"16748_CR11","doi-asserted-by":"publisher","unstructured":"Xu X, Li Y, Xu X, Wen Z, Che H, Liu S, Tao J (2014) Survey on discriminative feature selection for speech emotion recognition. In International Symposium on Chinese Spoken Language Processing, 345\u2013349. https:\/\/doi.org\/10.1109\/ISCSLP.2014.6936641","DOI":"10.1109\/ISCSLP.2014.6936641"},{"key":"16748_CR12","doi-asserted-by":"publisher","unstructured":"Gong C, Li X, Wu X (2014) Recurrent Neural Network Language Model with Part-of-speech for Mandarin Speech Recognition. In International Symposium on Chinese Spoken Language Processing, 459- 463. https:\/\/doi.org\/10.1109\/ISCSLP.2014.6936636","DOI":"10.1109\/ISCSLP.2014.6936636"},{"key":"16748_CR13","doi-asserted-by":"publisher","unstructured":"Shao P (2020) Chinese Speech Recognition System based on Deep Learning. In Journal of Physics: Conference Series, 1\u20136. https:\/\/doi.org\/10.1088\/1742-6596\/1549\/2\/022012","DOI":"10.1088\/1742-6596\/1549\/2\/022012"},{"key":"16748_CR14","unstructured":"Ropke W, Radulescu R, Efthymiadis K, Nowe A (2019) Training a Speech-to-Text Model for Dutch on the Corpus Gesproken Nederlands. In Proceedings of the Reference AI & ML Conference for Belgium, Netherlands & Luxemburg, 2491"},{"key":"16748_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5123671","volume":"2021","author":"G Singh","year":"2021","unstructured":"Singh G, Sharma S, Kumar V, Kaur M, Baz M, Masud M (2021) Spoken Language Identification Using Deep Learning. Hindawi Comput Intell Neurosci 2021:1\u201312. https:\/\/doi.org\/10.1155\/2021\/5123671","journal-title":"Hindawi Comput Intell Neurosci"},{"key":"16748_CR16","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.csl.2020.101158","volume":"66","author":"P Smit","year":"2020","unstructured":"Smit P, Virpioja S, Kurimo M (2020) Advances in subword-based HMM-DNN speech recognition across languages. Comput Speech Lang 66:101\u2013158. https:\/\/doi.org\/10.1016\/j.csl.2020.101158","journal-title":"Comput Speech Lang"},{"key":"16748_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.socl.2021.100018","author":"P Berjon","year":"2021","unstructured":"Berjon P, Nag A, Dev S (2021) Analysis of French Phonetic Idiosyncrasies for Accent Recognition. Soft Comput Lett. https:\/\/doi.org\/10.1016\/j.socl.2021.100018","journal-title":"Soft Comput Lett"},{"key":"16748_CR18","doi-asserted-by":"publisher","unstructured":"Yang H, Oehlke C, Meinel C (2011) German Speech Recognition: A Solution for the Analysis and Processing of Lecture Recordings. In Proc. of 10th IEEE\/ACIS International Conference on Computer and Information Science. https:\/\/doi.org\/10.1109\/ICIS.2011.38","DOI":"10.1109\/ICIS.2011.38"},{"key":"16748_CR19","doi-asserted-by":"publisher","unstructured":"Xu J, Matta K, Islam S, Nurnberger A (2020) German Speech Recognition System using Deep Speech. In International Conference on Natural Language Processing and Information Retrieval, 102\u2013106. https:\/\/doi.org\/10.1145\/3443279.3443313","DOI":"10.1145\/3443279.3443313"},{"key":"16748_CR20","doi-asserted-by":"publisher","unstructured":"Milde B, Kohn M (2018) Open-Source Automatic Speech Recognition for German. In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, Computation and Language. https:\/\/doi.org\/10.48550\/arXiv.1807.10311","DOI":"10.48550\/arXiv.1807.10311"},{"key":"16748_CR21","first-page":"550","volume":"13","author":"F Pantazoglou","year":"2019","unstructured":"Pantazoglou F, Kladis G, Papadakis N (2019) A Greek voice recognition interface for ROV applications, using machine learning technologies and the CMU Sphinx platform. Wseas Transact Syst Control 13:550\u2013560","journal-title":"Wseas Transact Syst Control"},{"key":"16748_CR22","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1023\/A:1026515132762","volume":"3","author":"M Szarvas","year":"2000","unstructured":"Szarvas M, Fegyo T, Mihajlik P, Tatai P (2000) Automatic Recognition of Hungarian: Theory and Practice. Int J Speech Technol 3:237\u2013251. https:\/\/doi.org\/10.1023\/A:1026515132762","journal-title":"Int J Speech Technol"},{"issue":"25","key":"16748_CR23","first-page":"1","volume":"9","author":"J Chen","year":"2020","unstructured":"Chen J, Nishimura R, Kitaoka N (2020) End-to-end recognition of streaming Japanese speech using CTC and local attention. In SIP 9(25):1\u20137","journal-title":"In SIP"},{"key":"16748_CR24","doi-asserted-by":"publisher","unstructured":"Mu D, Zhu T, Xu G, Li H, Guo D, Liu Y (2019) Attention-Based Speech Model for Japanese Recognization. In IEEE International Conference on Smart Internet of Things (SmartIoT), 402\u2013406. https:\/\/doi.org\/10.1109\/SmartIoT.2019.00071","DOI":"10.1109\/SmartIoT.2019.00071"},{"key":"16748_CR25","doi-asserted-by":"publisher","unstructured":"Abdallah A, Hamada M, Nurseitov D (2020) Attention-Based Fully Gated CNN-BGRU for Russian Handwritten Text. J Imaging, 6(141), 1\u201323. https:\/\/doi.org\/10.48550\/arXiv.2008.05373","DOI":"10.48550\/arXiv.2008.05373"},{"key":"16748_CR26","doi-asserted-by":"publisher","first-page":"11","DOI":"10.5815\/ijitcs.2018.08.02","volume":"8","author":"V Gazeau","year":"2018","unstructured":"Gazeau V, Varol C (2018) Automatic Spoken Language Recognition with Neural Networks. Int J Inf Technol Comput Sci 8:11\u201317. https:\/\/doi.org\/10.5815\/ijitcs.2018.08.02","journal-title":"Int J Inf Technol Comput Sci"},{"issue":"4","key":"16748_CR27","doi-asserted-by":"publisher","first-page":"893","DOI":"10.1007\/s10772-020-09768-x","volume":"23","author":"H Veisi","year":"2020","unstructured":"Veisi H, Mani A (2020) Persian speech recognition using deep learning. Int J Speech Technol 23(4):893\u2013905. https:\/\/doi.org\/10.1007\/s10772-020-09768-x","journal-title":"Int J Speech Technol"},{"key":"16748_CR28","doi-asserted-by":"publisher","unstructured":"Savargiv M, Bastanfard A (2015) Persian Speech Emotion Recognition. In IKT2015 7th International Conference on Information and Knowledge Technology, 1\u20135. https:\/\/doi.org\/10.1109\/IKT.2015.7288756","DOI":"10.1109\/IKT.2015.7288756"},{"key":"16748_CR29","unstructured":"Park K. Dutch: Single Speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/datasets\/bryanpark\/dutch-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR30","unstructured":"Park K. French: Single Speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/bryanpark\/french-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR31","unstructured":"Park K. German: Single speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/bryanpark\/german-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR32","unstructured":"Park K. Greek: Single Speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/bryanpark\/greek-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR33","unstructured":"Park K. Hungarian: Single Speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/bryanpark\/hungarian-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR34","unstructured":"Park K. Japanese: Single Speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/bryanpark\/japanese-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR35","unstructured":"Park K. Russian: Single Speaker Speech Dataset. Available [Online]:https:\/\/www.kaggle.com\/bryanpark\/russian-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR36","unstructured":"Park K. Spanish: Single Speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/bryanpark\/spanish-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR37","unstructured":"Park K. Finnish: Single Speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/datasets\/bryanpark\/finnish-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR38","unstructured":"Park K. Chinese: Single Speaker Speech Dataset. Available [Online]: https:\/\/www.kaggle.com\/bryanpark\/chinese-single-speaker-speech-dataset. Accessed 3 Feb 2022"},{"key":"16748_CR39","unstructured":"Persian dataset, Persian Speech. Available [Online]: https:\/\/github.com\/persiandataset\/PersianSpeech. Accessed 3 Feb 2022"},{"key":"16748_CR40","doi-asserted-by":"publisher","first-page":"40635","DOI":"10.1007\/s11042-022-12953-6","volume":"81","author":"P Antoniadis","year":"2022","unstructured":"Antoniadis P, Tsardoulias E, Symeonidis A (2022) A mechanism for personalized Automatic Speech Recognition for less frequently spoken languages: the Greek case. Multimedia Tools Appl 81:40635\u201340652. https:\/\/doi.org\/10.1007\/s11042-022-12953-6","journal-title":"Multimedia Tools Appl"},{"key":"16748_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06003-9","author":"N Jain","year":"2021","unstructured":"Jain N, Gupta V, Shubham, Madan A, Chaudhary A, Santosh K (2021) Understanding cartoon emotion using integrated deep neural network on large dataset. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06003-9","journal-title":"Neural Comput Appl"},{"key":"16748_CR42","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-022-00680-6","author":"G Kaur","year":"2023","unstructured":"Kaur G, Sharma A (2023) A deep learning-based model using hybrid feature extraction approach for consumer sentiment analysis. J Big Data. https:\/\/doi.org\/10.1186\/s40537-022-00680-6","journal-title":"J Big Data"},{"key":"16748_CR43","doi-asserted-by":"publisher","first-page":"13307","DOI":"10.1007\/s11042-022-13645-x","volume":"82","author":"A Kaur","year":"2023","unstructured":"Kaur A, Singh A, Sachdeva R, Kukreja V (2023) Automatic speech recognition systems: A survey of discriminative techniques. Multimed Tools Appl 82:13307\u201313339. https:\/\/doi.org\/10.1007\/s11042-022-13645-x","journal-title":"Multimed Tools Appl"},{"key":"16748_CR44","doi-asserted-by":"publisher","first-page":"22231","DOI":"10.1007\/s11042-021-10767-6","volume":"80","author":"K Al-karawi","year":"2021","unstructured":"Al-karawi K, Mohammed D (2021) Improving short utterance speaker verification by combining MFCC and Entrocy in Noisy conditions. Multimed Tools Appl 80:22231\u201322249. https:\/\/doi.org\/10.1007\/s11042-021-10767-6","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\/s11042-023-16748-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16748-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16748-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T08:43:54Z","timestamp":1709801034000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16748-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,15]]},"references-count":44,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16748"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16748-1","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,15]]},"assertion":[{"value":"20 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 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":"15 September 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":"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"}}]}}