|Title:||Multi-source domain adaptation via domain adversarial neural networks for xvector-based speaker recognition|
|Advisors:||Mak, M. W. (EIE)|
|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|
|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|
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