Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/95585
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | en_US |
dc.creator | Lin, WW | en_US |
dc.creator | Mak, MW | en_US |
dc.creator | Chien, JT | en_US |
dc.date.accessioned | 2022-09-22T06:13:59Z | - |
dc.date.available | 2022-09-22T06:13:59Z | - |
dc.identifier.issn | 2329-9290 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/95585 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_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.rights | The following publication W. Lin, M. Mak and J. Chien, "Multisource I-Vectors Domain Adaptation Using Maximum Mean Discrepancy Based Autoencoders," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 26, no. 12, pp. 2412-2422, Dec. 2018 is available at https://doi.org/10.1109/TASLP.2018.2866707. | en_US |
dc.subject | Domain adaptation | en_US |
dc.subject | I-vectors | en_US |
dc.subject | Maximum mean discrepancy | en_US |
dc.subject | Speaker verification | en_US |
dc.title | Multisource i-vectors domain adaptation using maximum mean discrepancy based autoencoders | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.description.otherinformation | Title in author file: Multi-source I-vectors Domain Adaptation using Maximum Mean Discrepancy Based Autoencoders | en_US |
dc.identifier.spage | 2412 | en_US |
dc.identifier.epage | 2422 | en_US |
dc.identifier.volume | 26 | en_US |
dc.identifier.issue | 12 | en_US |
dc.identifier.doi | 10.1109/TASLP.2018.2866707 | en_US |
dcterms.abstract | Like many machine learning tasks, the performance of speaker verification (SV) systems degrades when training and test data come from very different distributions. What's more, both training and test data themselves could be composed of heterogeneous subsets. These multisource mismatches are detrimental to SV performance. This paper proposes incorporating maximum mean discrepancy (MMD) into the loss function of autoencoders to reduce these mismatches. MMD is a nonparametric method for measuring the distance between two probability distributions. With a properly chosen kernel, MMD can match up to infinite moments of data distributions. We generalize MMD to measure the discrepancies of multiple distributions.We call the generalized MMDdomainwiseMMD. Using domainwiseMMDas an objective function, we propose two autoencoders, namely nuisance-attribute autoencoder (NAE) and domain-invariant autoencoder (DAE), for multisource i-vector adaptation. NAE encodes the features that cause most of the multisource mismatch measured by domainwise MMD. DAE directly encodes the features that minimize the multisource mismatch. Using these MMD-based autoencoders as a preprocessing step for PLDA training, we achieve a relative improvement of 19.2% EER on the NIST 2016 SRE compared to PLDA without adaptation. We also found that MMD-based autoencoders are more robust to unseen domains. In the domain robustness experiments, MMD-based autoencoders show 6.8% and 5.2% improvements over IDVC on female and male Cantonese speakers, respectively. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE/ACM transactions on audio, speech, and language processing, Dec. 2018, v. 26, no. 12, 8445620, p. 2412-2422 | en_US |
dcterms.isPartOf | IEEE/ACM transactions on audio, speech, and language processing | en_US |
dcterms.issued | 2018-12 | - |
dc.identifier.scopus | 2-s2.0-85052690121 | - |
dc.identifier.eissn | 2329-9304 | en_US |
dc.identifier.artn | 8445620 | en_US |
dc.description.validate | 202209_bcww | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0437 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20150384 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Lin_Multisource_I-Vectors_Domain.pdf | Pre-Published version | 1.28 MB | Adobe PDF | View/Open |
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