Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/43678
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | en_US |
dc.creator | Pang, X | en_US |
dc.creator | Mak, MW | en_US |
dc.date.accessioned | 2016-06-07T06:22:55Z | - |
dc.date.available | 2016-06-07T06:22:55Z | - |
dc.identifier.issn | 1381-2416 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/43678 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.rights | © Springer Science+Business Media New York 2015 | en_US |
dc.rights | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use(https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10772-015-9310-8 | en_US |
dc.subject | Fusion | en_US |
dc.subject | I-vectors | en_US |
dc.subject | NIST 2012 SRE | en_US |
dc.subject | Noise robustness | en_US |
dc.subject | Probabilistic LDA | en_US |
dc.subject | Speaker verification | en_US |
dc.title | Noise robust speaker verification via the fusion of SNR-independent and SNR-dependent PLDA | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 633 | en_US |
dc.identifier.epage | 648 | en_US |
dc.identifier.volume | 18 | en_US |
dc.identifier.issue | 4 | en_US |
dc.identifier.doi | 10.1007/s10772-015-9310-8 | en_US |
dcterms.abstract | While i-vectors with probabilistic linear discriminant analysis (PLDA) can achieve state-of-the-art performance in speaker verification, the mismatch caused by acoustic noise remains a key factor affecting system performance. In this paper, a fusion system that combines a multi-condition signal-to-noise ratio (SNR)-independent PLDA model and a mixture of SNR-dependent PLDA models is proposed to make speaker verification systems more noise robust. First, the whole range of SNR that a verification system is expected to operate is divided into several narrow ranges. Then, a set of SNR-dependent PLDA models, one for each narrow SNR range, are trained. During verification, the SNR of the test utterance is used to determine which of the SNR-dependent PLDA models is used for scoring. To further enhance performance, the SNR-dependent and SNR-independent models are fused using linear and logistic regression fusion. The performance of the fusion system and the SNR-dependent system is evaluated on the NIST 2012 speaker recognition evaluation for both noisy and clean conditions. Results show that a mixture of SNR-dependent PLDA models perform better in both clean and noisy conditions. It was also found that the fusion system is more robust than the conventional i-vector/PLDA systems under noisy conditions. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | International journal of speech technology, Dec. 2015, v. 18, no. 4, p. 633-648 | en_US |
dcterms.isPartOf | International journal of speech technology | en_US |
dcterms.issued | 2015-12 | - |
dc.identifier.scopus | 2-s2.0-84947485041 | - |
dc.identifier.rosgroupid | 2015002456 | - |
dc.description.ros | 2015-2016 > Academic research: refereed > Publication in refereed journal | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | RGC-B3-0969 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Noise_Robust_Speaker.pdf | Pre-Published version | 1.44 MB | Adobe PDF | View/Open |
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