{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:16:01Z","timestamp":1743030961882,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031441974"},{"type":"electronic","value":"9783031441981"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-44198-1_32","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:02:34Z","timestamp":1695283354000},"page":"384-394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Limited Resource Speech Recognition Performance with\u00a0Latent Regression Bayesian Network"],"prefix":"10.1007","author":[{"given":"Liang","family":"Xu","sequence":"first","affiliation":[]},{"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xiaona","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yigang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"32_CR1","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, M.B., O\u011fuz, K.: Speech emotion recognition: emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers. Speech Commun. 116, 56\u201376 (2020)","journal-title":"Speech Commun."},{"key":"32_CR2","doi-asserted-by":"crossref","unstructured":"Bernard, M., Poli, M., Karadayi, J., Dupoux, E.: Shennong: a python toolbox for audio speech features extraction. Behav. Res. Methods. 1\u201313 (2023)","DOI":"10.3758\/s13428-022-02029-6"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Dong, L., Xu, S., Xu, B.: Speech-transformer: a no-recurrence sequence-to-sequence model for speech recognition. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5884\u20135888. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8462506"},{"key":"32_CR4","doi-asserted-by":"crossref","unstructured":"Gr\u00e9zl, F., Karafi\u00e1t, M., Kont\u00e1r, S., Cernocky, J.: Probabilistic and bottle-neck features for LVCSR of meetings. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP 2007, vol. 4, pp. IV-757. IEEE (2007)","DOI":"10.1109\/ICASSP.2007.367023"},{"key":"32_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.csl.2020.101098","volume":"65","author":"E Hermann","year":"2021","unstructured":"Hermann, E., Kamper, H., Goldwater, S.: Multilingual and unsupervised subword modeling for zero-resource languages. Comput. Speech Lang. 65, 101098 (2021)","journal-title":"Comput. Speech Lang."},{"key":"32_CR6","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448\u2013456. PMLR (2015)"},{"key":"32_CR7","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"32_CR8","unstructured":"Mozilla: Mozilla common voice. https:\/\/commonvoice.mozilla.org. Accessed 21 Feb 2023"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Nie, S., Zhao, Y., Ji, Q.: Latent regression Bayesian network for data representation. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 3494\u20133499. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7900175"},{"issue":"1","key":"32_CR10","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/MSP.2017.2763440","volume":"35","author":"S Nie","year":"2018","unstructured":"Nie, S., Zheng, M., Ji, Q.: The deep regression Bayesian network and its applications: probabilistic deep learning for computer vision. IEEE Signal Process. Mag. 35(1), 101\u2013111 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"32_CR11","unstructured":"Padhi, T., Biswas, A., De Wet, F., van der Westhuizen, E., Niesler, T.: Multilingual bottleneck features for improving ASR performance of code-switched speech in under-resourced languages. arXiv preprint arXiv:2011.03118 (2020)"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Silnova, A., et al.: But\/Phonexia bottleneck feature extractor. In: Odyssey, pp. 283\u2013287 (2018)","DOI":"10.21437\/Odyssey.2018-40"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"1","key":"32_CR14","first-page":"19","volume":"1","author":"V Tiwari","year":"2010","unstructured":"Tiwari, V.: MFCC and its applications in speaker recognition. Int. J. Emerg. Technol. 1(1), 19\u201322 (2010)","journal-title":"Int. J. Emerg. Technol."},{"key":"32_CR15","unstructured":"Wang, D., Zhang, X.: THCHS-30: a free Chinese speech corpus. arXiv preprint arXiv:1512.01882 (2015)"},{"issue":"3","key":"32_CR16","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1109\/TIP.2018.2878339","volume":"28","author":"S Wang","year":"2018","unstructured":"Wang, S., Hao, L., Ji, Q.: Facial action unit recognition and intensity estimation enhanced through label dependencies. IEEE Trans. Image Process. 28(3), 1428\u20131442 (2018)","journal-title":"IEEE Trans. Image Process."},{"issue":"4","key":"32_CR17","doi-asserted-by":"publisher","first-page":"1084","DOI":"10.1109\/TMM.2019.2934824","volume":"22","author":"S Wang","year":"2019","unstructured":"Wang, S., Hao, L., Ji, Q.: Knowledge-augmented multimodal deep regression Bayesian networks for emotion video tagging. IEEE Trans. Multimed. 22(4), 1084\u20131097 (2019)","journal-title":"IEEE Trans. Multimed."},{"key":"32_CR18","unstructured":"Zeroth Project: Zeroth-Korean: Korean speech recognition corpus for zeroth ASR (2023). https:\/\/www.openslr.org\/61\/. Accessed 21 Feb 2023"},{"issue":"2\u20133","key":"32_CR19","first-page":"297","volume":"22","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., et al.: An open speech resource for Tibetan multi-dialect and multitask recognition. Int. J. Comput. Sci. Eng. 22(2\u20133), 297\u2013304 (2020)","journal-title":"Int. J. Comput. Sci. Eng."},{"issue":"1","key":"32_CR20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.32604\/jiot.2019.05866","volume":"1","author":"Y Zhao","year":"2019","unstructured":"Zhao, Y., et al.: Tibetan multi-dialect speech recognition using latent regression Bayesian network and end-to-end mode. J. Internet of Things 1(1), 17 (2019)","journal-title":"J. Internet of Things"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44198-1_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T15:32:22Z","timestamp":1730129542000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44198-1_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441974","9783031441981"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44198-1_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"426","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"22","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"type of other papers accepted : 9 Abstract","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}