{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:55:42Z","timestamp":1760144142581,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Sciences and Engineering Research Council (NSERC) Discovery Grant funding"},{"name":"NSERC Strategic Partnership Grant (SPG)"},{"name":"University of Calgary Transdisciplinary Connector Funding"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Speaker recognition is a challenging problem in behavioral biometrics that has been rigorously investigated over the last decade. Although numerous supervised closed-set systems inherit the power of deep neural networks, limited studies have been made on open-set speaker recognition. This paper proposes a self-supervised open-set speaker recognition that leverages the geometric properties of speaker distribution for accurate and robust speaker verification. The proposed framework consists of a deep neural network incorporating a wider viewpoint of temporal speech features and Laguerre\u2013Voronoi diagram-based speech feature extraction. The deep neural network is trained with a specialized clustering criterion that only requires positive pairs during training. The experiments validated that the proposed system outperformed current state-of-the-art methods in open-set speaker recognition and cluster representation.<\/jats:p>","DOI":"10.3390\/s24061996","type":"journal-article","created":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T06:35:30Z","timestamp":1711002930000},"page":"1996","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Self-Supervised Open-Set Speaker Recognition with Laguerre\u2013Voronoi Descriptors"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7375-9040","authenticated-orcid":false,"given":"Abu Quwsar","family":"Ohi","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Calgary, Calgary, AB T2N1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5338-1834","authenticated-orcid":false,"given":"Marina L.","family":"Gavrilova","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Calgary, Calgary, AB T2N1N4, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"ref_1","unstructured":"Balestriero, R., Ibrahim, M., Sobal, V., Morcos, A., Shekhar, S., Goldstein, T., Bordes, F., Bardes, A., Mialon, G., and Tian, Y. 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