Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.contributor.advisor | Mak, M. W. (EIE) | en_US |
| dc.creator | Ouyang, Haowen | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/11189 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Multi-source domain adaptation via domain adversarial neural networks for xvector-based speaker recognition | en_US |
| dcterms.abstract | The x-vector/PLDA framework has achieved state-of-the-art performance when the training and test data come from the same domain. However, verifcation error remains high when the x-vectors of the target speakers and the test speakers are extracted from different domains. This dissertation uses domain adversarial training (DAT) and domain adversarial neural networks (DANNs) to produce domain-invariant speaker embeddings from domain-mismatched x-vectors while maintaining the speaker-discriminative nature of the x-vectors. Conventional DANNs use DAT to optimize a feature extractor to produce domain-invariant features that confuse a binary domain classifer, where the latter aims to determine whether the input utterance comes from the source domain or the target domain. The proposed DANN achieves multi-source DAT by modifying domain classifer from binary to multi-class classifcation. Experimental results on NIST 2016 and 2018 SRE show that the proposed DANN can produce speaker embeddings that achieve the lowest equal error rate compared to the conventional x-vectors. | en_US |
| dcterms.extent | ix, 40 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2021 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Automatic speech recognition | en_US |
| dcterms.LCSH | Speech processing systems | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5661.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.04 MB | Adobe PDF | View/Open |
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