Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107247
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | - |
dc.creator | Li, N | en_US |
dc.creator | Mak, MW | en_US |
dc.creator | Chien, JT | en_US |
dc.date.accessioned | 2024-06-13T01:04:52Z | - |
dc.date.available | 2024-06-13T01:04:52Z | - |
dc.identifier.isbn | 978-1-5090-4903-5 (Electronic) | en_US |
dc.identifier.isbn | 978-1-5090-4904-2 (Print on Demand(PoD)) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107247 | - |
dc.description | 2016 IEEE Spoken Language Technology Workshop (SLT), 13-16 December 2016, San Diego, CA, USA | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | ©2016 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.rights | The following publication N. Li, M. -W. Mak and J. -T. Chien, "Deep neural network driven mixture of PLDA for robust i-vector speaker verification," 2016 IEEE Spoken Language Technology Workshop (SLT), San Diego, CA, USA, 2016, pp. 186-191 is available at https://doi.org/10.1109/SLT.2016.7846263. | en_US |
dc.subject | Deep neural networks | en_US |
dc.subject | I-vector | en_US |
dc.subject | Mixture of PLDA | en_US |
dc.subject | SNR mismatch | en_US |
dc.subject | Speaker verification | en_US |
dc.title | Deep neural network driven mixture of PLDA for robust i-vector speaker verification | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 186 | en_US |
dc.identifier.epage | 191 | en_US |
dc.identifier.doi | 10.1109/SLT.2016.7846263 | en_US |
dcterms.abstract | In speaker recognition, the mismatch between the enrollment and test utterances due to noise with different signal-to-noise ratios (SNRs) is a great challenge. Based on the observation that noise-level variability causes the i-vectors to form heterogeneous clusters, this paper proposes using an SNR-aware deep neural network (DNN) to guide the training of PLDA mixture models. Specifically, given an i-vector, the SNR posterior probabilities produced by the DNN are used as the posteriors of indicator variables of the mixture model. As a result, the proposed model provides a more reasonable soft division of the i-vector space compared to the conventional mixture of PLDA. During verification, given a test trial, the marginal likelihoods from individual PLDA models are linearly combined by the posterior probabilities of SNR levels computed by the DNN. Experimental results for SNR mismatch tasks based on NIST 2012 SRE suggest that the proposed model is more effective than PLDA and conventional mixture of PLDA for handling heterogeneous corpora. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | In Proceedings of 2016 IEEE Spoken Language Technology Workshop (SLT), 13-16 December 2016, San Diego, CA, USA | en_US |
dcterms.issued | 2016 | - |
dc.identifier.scopus | 2-s2.0-85016063901 | - |
dc.relation.conference | IEEE Spoken Language Technology Workshop [SLT] | - |
dc.description.validate | 202403 bckw | - |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0741 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 9591755 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Conference Paper |
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Mak_Deep_Neural_Network.pdf | Pre-Published version | 375.59 kB | Adobe PDF | View/Open |
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