探討領域泛化於跨裝置語者驗證系統

Discussion on domain generalization in the cross-device speaker verification system

本論文運用領域泛化改進跨裝置語者驗證系統的效能，我們基於一個可訓練的語者驗證系統，利用領域泛化演算法微調模型參數。首先我們使用VoxCeleb2資料集訓練ECAPA-TDNN作為一個基準模型，接著利用CHT-TDSV資料集與以下領域泛化演算法來對其進行微調：DANN、CDANN、Deep CORAL。我們提出的系統在NSYSU-TDSV資料集中測試10種不同的模擬情境，包含單一裝置與多種裝置，最終於多個裝置的場景下，最佳等錯誤率從基礎模型的18.39下降至8.84，成功在語者驗證系統達到跨裝置辨識的成效。

In this paper, we use domain generalization to improve the performance of the cross-device speaker verification system. Based on a trainable speaker verification system, we use domain generalization algorithms to fine-tune the model parameters. First, we use the VoxCeleb2 dataset to train ECAPA-TDNN as a baseline model. Then, use the CHT-TDSV dataset and the following domain generalization algorithms to fine-tune it: DANN, CDNN, Deep CORAL. Our proposed system tests 10 different scenarios in the NSYSU-TDSV dataset, including a single device and multiple devices. Finally, in the scenario of multiple devices, the best equal error rate decreased from 18.39 in the baseline to 8.84. Successfully achieved cross-device identification on the speaker verification system.