| Author: | Ouyang, Haowen |
| Title: | Multi-source domain adaptation via domain adversarial neural networks for xvector-based speaker recognition |
| Advisors: | Mak, M. W. (EIE) |
| Degree: | M.Sc. |
| Year: | 2021 |
| Subject: | Automatic speech recognition Speech processing systems Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Electronic and Information Engineering |
| Pages: | ix, 40 pages : color illustrations |
| Language: | English |
| 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. |
| Rights: | All rights reserved |
| Access: | restricted access |
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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