{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T09:11:34Z","timestamp":1685351494992},"reference-count":0,"publisher":"National Library of Serbia","issue":"4","license":[{"start":{"date-parts":[[2012,1,1]],"date-time":"2012-01-01T00:00:00Z","timestamp":1325376000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2012]]},"abstract":"<jats:p>Information of speech units like vowels, consonants and syllables can be a\n   kind of knowledge used in text-independent Short Utterance Speaker\n   Recognition (SUSR) in a similar way as in text-dependent speaker recognition.\n   In such tasks, data for each speech unit, especially at the time of\n   recognition, is often not enough. Hence, it is not practical to use the full\n   set of speech units because some of the units might not be well trained. To\n   solve this problem, a method of using speech unit categories rather than\n   individual phones is proposed for SUSR, wherein similar speech units are put\n   together, hence solving the problem of sparse data. We define Vowel,\n   Consonant, and Syllable Categories (VC, CC and SC) with Standard Chinese\n   (Putonghua) as a reference. A speech utterance is recognized into VC, CC ad\n   SC sequences which are used to train Universal Background Models (UBM) for\n   each speech unit category in the training procedure, and to perform speech\n   unit category dependent speaker recognition, respectively. Experimental\n   results in Gaussian Mixture Model-Universal Background Model (GMM-UBM) based\n   system give a relative equal error rate (EER) reduction of 54.50% and 40.95%\n   from minimum EERs of VCs and SCs, respectively, for 2 seconds of test\n   utterance compared with the existing SUSR systems.<\/jats:p>","DOI":"10.2298\/csis120208053f","type":"journal-article","created":{"date-parts":[[2012,12,28]],"date-time":"2012-12-28T10:31:33Z","timestamp":1356690693000},"page":"1407-1430","source":"Crossref","is-referenced-by-count":3,"title":["Speech unit category based short utterance speaker recognition"],"prefix":"10.2298","volume":"9","author":[{"given":"Nakhat","family":"Fatima","sequence":"first","affiliation":[{"name":"Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Wu","sequence":"additional","affiliation":[{"name":"Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"Thomas","given":"Fang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Center for Speech and Language Technologies, Division of Technical Innovation and Development, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:30:48Z","timestamp":1685349048000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141200053F"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2012]]}},"URL":"https:\/\/doi.org\/10.2298\/csis120208053f","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012]]}}}