The NO_TRAIN_NO_GAIN system for O-COCOSDA and VLSP 2022 - A-MSV shared task: ASIAN multilingual speaker verification

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.