{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:33:00Z","timestamp":1775521980283,"version":"3.50.1"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030339036","type":"print"},{"value":"9783030339043","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-33904-3_4","type":"book-chapter","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T22:40:05Z","timestamp":1572043205000},"page":"42-47","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Representation Learning for Underdefined Tasks"],"prefix":"10.1007","author":[{"given":"Jean-Fran\u00e7ois","family":"Bonastre","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,22]]},"reference":[{"key":"4_CR1","doi-asserted-by":"crossref","unstructured":"Variani, E., Lei, X., McDermott, E., Moreno, I.L., Gonzalez-Dominguez, J.: Deep neural networks for small footprint text-dependent speaker verification. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2014)","DOI":"10.1109\/ICASSP.2014.6854363"},{"key":"4_CR2","unstructured":"Lopez-Paz, D., Bottou, L., Sch\u00f6lkopf, B., Vapnik, V.: Unifying distillation and privileged information. In: International Conference on Learning Representations (2016)"},{"key":"4_CR3","first-page":"2023","volume":"16","author":"V Vapnik","year":"2015","unstructured":"Vapnik, V., Izmailov, R.: Learning using privileged information: similarity control and knowledge transfer. J. Mach. Learn. Res. 16, 2023\u20132049 (2015)","journal-title":"J. Mach. Learn. Res."},{"key":"4_CR4","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015)"},{"key":"4_CR5","doi-asserted-by":"crossref","unstructured":"Price, R., Iso, K.-I., Shinoda, K.: Wise teachers train better DNN acoustic models. EURASIP J. Audio Speech Music Process. 2016 (2016)","DOI":"10.1186\/s13636-016-0088-7"},{"key":"4_CR6","doi-asserted-by":"crossref","unstructured":"Markov, K., Matsui, T.: Robust speech recognition using generalized distillation framework. In: INTERSPEECH (2016)","DOI":"10.21437\/Interspeech.2016-852"},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Li, J., Seltzer, M.L., Wang, X., Zhao, R., Gong, Y.: Large-scale domain adaptation via teacher-student learning (2017)","DOI":"10.21437\/Interspeech.2017-519"},{"key":"4_CR8","doi-asserted-by":"crossref","unstructured":"Watanabe, S., Hori, T., Le Roux, J., Hershey, J.R.: Student-teacher network learning with enhanced features. In: Acoustics, Speech and Signal Processing (ICASSP). IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7953163"},{"key":"4_CR9","doi-asserted-by":"crossref","unstructured":"Asami, T., Masumura, R., Yamaguchi, Y., Masataki, H., Aono, Y.: Domain adaptation of DNN acoustic models using knowledge distillation. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2017)","DOI":"10.1109\/ICASSP.2017.7953145"},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Joy, N.M., Kothinti, S.R., Umesh, S., Abraham, B.: Generalized distillation framework for speaker normalization. In: INTERSPEECH (2017)","DOI":"10.21437\/Interspeech.2017-874"},{"key":"4_CR11","doi-asserted-by":"crossref","unstructured":"Obin, N., Roebel, A., Bachman, G.: On automatic voice casting for expressive speech: speaker recognition vs. speech classification. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2014)","DOI":"10.1109\/ICASSP.2014.6853737"},{"key":"4_CR12","doi-asserted-by":"crossref","unstructured":"Gresse, A., Rouvier, M., Dufour, R., Labatut, V., Bonastre, J.-F.: Acoustic pairing of original and dubbed voices in the context of video game localization. In: INTERSPEECH (2017)","DOI":"10.21437\/Interspeech.2017-1311"},{"key":"4_CR13","doi-asserted-by":"crossref","unstructured":"Gresse, A., Quillot, M., Dufour, R., Labatut, V., Bonastre, J.-F.: Similarity metric based on Siamese neural networks for voice casting. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8683178"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33904-3_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:05:20Z","timestamp":1710252320000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33904-3_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030339036","9783030339043"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33904-3_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"22 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Havana","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuba","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ciarp2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ciarp.uci.cu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"128","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":"70","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":"0","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":"55% - 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","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":"3","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}