Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/88460
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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.creatorJiang, Yen_US
dc.creatorLeung, HFen_US
dc.date.accessioned2020-11-26T03:17:01Z-
dc.date.available2020-11-26T03:17:01Z-
dc.identifier.isbn978-1-5386-6811-5 (Electronic)en_US
dc.identifier.isbn978-1-5386-6810-8 (USB)en_US
dc.identifier.isbn978-1-5386-6812-2 (Print on Demand(PoD))en_US
dc.identifier.urihttp://hdl.handle.net/10397/88460-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication Y. Jiang and H. F. Frank Leung, "A Class-dependent Background Model for Speech Signal Feature Extraction," 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, 2018, pp. 1-5 is available at https://dx.doi.org/10.1109/ICDSP.2018.8631583en_US
dc.subjectClass-dependent background modelen_US
dc.subjectUniversal background modelen_US
dc.subjectGaussian supervectoren_US
dc.subjectI-vectoren_US
dc.subjectSpeech signal classificationen_US
dc.titleA class-dependent background model for speech signal feature extractionen_US
dc.typeConference Paperen_US
dc.identifier.spage1en_US
dc.identifier.epage5en_US
dc.identifier.doi10.1109/ICDSP.2018.8631583en_US
dcterms.abstractUniversal Background Model (UBM) has been successfully applied to many speech signal classification tasks, such as speaker recognition and microphone recognition. UBM is used to form Gaussian Supervector (GSV) or i-vector, which is a good feature vector representing a piece of speech signal. In this paper, we propose another background model called Class-dependent Background Model (CBM), which makes use of class labels. UBM is completely a generative model, while CBM can be both generative and discriminative. Under some conditions, CBM can consume less time to be constructed than UBM. We also compare the performance of UBM and CBM as the background model to form GSV and i-vector for doing speaker recognition, microphone recognition, and telephone session recognition. Experimental results show that CBM performs very well and can be even better than UBM in most cases.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), Shanghai, China, China, 19-21 Nov. 2018, p. 1-5en_US
dcterms.issued2018-11-
dc.relation.conferenceIEEE International Conference on Digital Signal Processing [DSP]en_US
dc.description.validate202011 bcrcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0512-n03en_US
dc.description.pubStatusPublisheden_US
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