{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:06:16Z","timestamp":1742911576706,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031098499"},{"type":"electronic","value":"9783031098505"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-09850-5_5","type":"book-chapter","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T23:02:48Z","timestamp":1656284568000},"page":"65-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automatic Speech Recognition Model Adaptation to\u00a0Medical Domain Using Untranscribed Audio"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0165-0868","authenticated-orcid":false,"given":"Askars","family":"Salimbajevs","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8402-4549","authenticated-orcid":false,"given":"Jurgita","family":"Kapo\u010di\u016bt\u0117-Dzikien\u0117","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, W., Wang, C.: Semi-supervised ASR by end-to-end self-training. arXiv abs\/2001.09128 (2020)","DOI":"10.21437\/Interspeech.2020-1280"},{"key":"5_CR2","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.procs.2019.01.069","volume":"148","author":"R Errattahi","year":"2019","unstructured":"Errattahi, R., El Hannani, A., Salmam, F.Z., Ouahmane, H.: Incorporating label dependency for ASR error detection via RNN. Procedia Comput. Sci. 148, 266\u2013272 (2019)","journal-title":"Procedia Comput. Sci."},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Grezl, F., Karafi\u00e1t, M.: Semi-supervised bootstrapping approach for neural network feature extractor training. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 470\u2013475. IEEE (2013)","DOI":"10.1109\/ASRU.2013.6707775"},{"key":"5_CR4","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/978-981-16-6309-3_27","volume-title":"Intelligent Sustainable Systems","author":"N Gruzitis","year":"2022","unstructured":"Gruzitis, N., Dargis, R., Lasmanis, V.J., Garkaje, G., Gosko, D.: Adapting automatic speech recognition to the radiology domain for a less-resourced language: the case of Latvian. In: Nagar, A.K., Jat, D.S., Mar\u00edn-Ravent\u00f3s, G., Mishra, D.K. (eds.) Intelligent Sustainable Systems. LNNS, vol. 333, pp. 267\u2013276. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-6309-3_27"},{"key":"5_CR5","unstructured":"Heafield, K.: Kenlm: faster and smaller language model queries. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, pp. 187\u2013197 (2011)"},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Kahn, J., Lee, A., Hannun, A.: Self-training for end-to-end speech recognition. In: ICASSP 2020\u20132020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7084\u20137088. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9054295"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Khonglah, B.K., Madikeri, S.R., Dey, S., Bourlard, H., Motl\u00edcek, P., Billa, J.: Incremental semi-supervised learning for multi-genre speech recognition. In: ICASSP, pp. 7419\u20137423. IEEE (2020)","DOI":"10.1109\/ICASSP40776.2020.9054309"},{"key":"5_CR8","unstructured":"Lybarger, K., Ostendorf, M., Yetisgen, M.: Automatically detecting likely edits in clinical notes created using automatic speech recognition. In: AMIA Annual Symposium Proceedings, vol. 2017, p. 1186. American Medical Informatics Association (2017)"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Manohar, V., Hadian, H., Povey, D., Khudanpur, S.: Semi-supervised training of acoustic models using lattice-free mmi. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4844\u20134848. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8462331"},{"key":"5_CR10","unstructured":"Pinnis, M., Auzi\u0146a, I., Goba, K.: Designing the Latvian speech recognition corpus. In: Proceedings of the 9th Edition of the Language Resources and Evaluation Conference (LREC 2014), pp. 1547\u20131553 (2014)"},{"key":"5_CR11","unstructured":"Pinnis, M., Salimbajevs, A., Auzina, I.: Designing a speech corpus for the development and evaluation of dictation systems in Latvian. In: Chair, N.C.C., et al. (eds.) Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). European Language Resources Association (ELRA), Paris, France (2016)"},{"key":"5_CR12","unstructured":"Povey, D., et al.: The kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. IEEE Signal Processing Society, December 2011. iEEE Catalog No.: CFP11SRW-USB"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Povey, D., et al.: Purely sequence-trained neural networks for ASR based on lattice-free MMI. In: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, vol. 08-12-Sept, pp. 2751\u20132755 (2016). https:\/\/doi.org\/10.21437\/Interspeech.2016-595","DOI":"10.21437\/Interspeech.2016-595"},{"key":"5_CR14","unstructured":"Salimbajevs, A.: Creating lithuanian and Latvian speech corpora from inaccurately annotated web data. In: Calzolari, N., et al. (eds.) Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, 7\u201312 May 2018. European Language Resources Association (ELRA) (2018). http:\/\/www.lrec-conf.org\/proceedings\/lrec2018\/summaries\/258.html"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 1715\u20131725 (2016)","DOI":"10.18653\/v1\/P16-1162"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Sheikh, I., Vincent, E., Illina, I.: On semi-supervised LF-MMI training of acoustic models with limited data. In: INTERSPEECH 2020, Shanghai, China (2020). https:\/\/hal.inria.fr\/hal-02907924","DOI":"10.21437\/Interspeech.2020-2242"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Singh, K., et al.: Large scale weakly and semi-supervised learning for low-resource video ASR. In: INTERSPEECH, pp. 3770\u20133774. ISCA (2020)","DOI":"10.21437\/Interspeech.2020-1917"},{"key":"5_CR18","unstructured":"Synnaeve, G., et al.: End-to-end ASR: from supervised to semi-supervised learning with modern architectures. CoRR abs\/1911.08460 (2019). http:\/\/arxiv.org\/abs\/1911.08460"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Tam, Y.C., Lei, Y., Zheng, J., Wang, W.: ASR error detection using recurrent neural network language model and complementary ASR. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2312\u20132316. IEEE (2014)","DOI":"10.1109\/ICASSP.2014.6854012"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Thomas, S., Seltzer, M.L., Church, K., Hermansky, H.: Deep neural network features and semi-supervised training for low resource speech recognition. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6704\u20136708. IEEE (2013)","DOI":"10.1109\/ICASSP.2013.6638959"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Vesel\u1ef3, K., Burget, L., Cernock\u1ef3, J.: Semi-supervised DNN training with word selection for ASR. In: Interspeech, pp. 3687\u20133691 (2017)","DOI":"10.21437\/Interspeech.2017-1385"},{"key":"5_CR22","doi-asserted-by":"publisher","unstructured":"Wallington, E., Kershenbaum, B., Klejch, O., Bell, P.: On the learning dynamics of semi-supervised training for ASR. In: Proceedings of Interspeech 2021, pp. 716\u2013720 (2021). https:\/\/doi.org\/10.21437\/Interspeech.2021-1777","DOI":"10.21437\/Interspeech.2021-1777"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, P., Liu, Y., Hain, T.: Semi-supervised DNN training in meeting recognition. In: 2014 IEEE Spoken Language Technology Workshop (SLT), pp. 141\u2013146. IEEE (2014)","DOI":"10.1109\/SLT.2014.7078564"}],"container-title":["Communications in Computer and Information Science","Digital Business and Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-09850-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:41:13Z","timestamp":1709833273000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-09850-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031098499","9783031098505"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-09850-5_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 June 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Baltic DB&IS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Baltic Conference on Digital Business and Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Riga","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Latvia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dbis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dbis2022.lu.lv\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42","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":"16","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":"1","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":"38% - 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":"3,5","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":"1,8","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}