Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/117419
Title: The NO_TRAIN_NO_GAIN system for O-COCOSDA and VLSP 2022 - A-MSV shared task: ASIAN multilingual speaker verification
Authors: Nguyen, Ngoc Dung
Ly, Nhat Nam
Le, Trong Khanh
Keywords: Speaker verification
ECAPA-TDNN
GMM
Fine-tuning
Score normalization
Issue Date: 2024
Series/Report no.: Tạp chí Tin học và Điều khiển học (Journal of Computer Science and Cybernetics);Vol.40, No.01 .- P.67-77
Abstract: This paper proposes a semi-supervised multilingual speaker verification (MSV) system submitted for the 2 tasks, MSV for the Asian language inside the training set (T01) and outside the training set (T02) in O-COCOSDA and VLSP challenge 2022. To solve the problem, our strategy is training a baseline acoustic model with given labeled data (MSV CommonVoice) and fine-tuning the trained acoustic model with both given labeled data and given unlabeled data (MSV Youtube). To achieve the fine-tuning step, the unlabeled data is converted to labeled data by pseudo labeling technique using the clustering method with the embedding vectors extracted from the trained acoustic model. Besides, we also apply test-time augmentation, back-end scoring, and score normalization with the AS-Norm technique to improve the result. When evaluated on the VLSP 2022 challenge's given test set, our best system with baseline ECAPA-TDNN achieves an equal error rate (EER) of 2.296% in T01 and 3.3296% in T02, which ranks second rank in both two tasks.
URI: https://dspace.ctu.edu.vn/jspui/handle/123456789/117419
ISSN: 1813-9663
Appears in Collections:Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

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