| DC 欄位 |
值 |
語言 |
| DC.contributor | 電機工程學系 | zh_TW |
| DC.creator | 陳登國 | zh_TW |
| DC.creator | Tran Dang Khoa | en_US |
| dc.date.accessioned | 2021-1-27T07:39:07Z | |
| dc.date.available | 2021-1-27T07:39:07Z | |
| dc.date.issued | 2021 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=107521604 | |
| dc.contributor.department | 電機工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 在本文中，我們提出了一種GRU結構的變體，稱為雙序列門控注意單元（DS-GAU），其中計算了x向量基線的每個TDNN層的統計池，並將其通過DS-GAU層傳遞， 在訓練為幀級時從輸入要素的不同時間上下文中聚合更多信息。 我們提出的架構在VoxCeleb2數據集上進行了訓練，其中特徵向量稱為DSGAU-向量。 我們對VoxCeleb1數據集和“野生演說者”（SITW）數據集進行了評估，並將實驗結果與x矢量基線系統進行了比較。 結果表明，相對於VoxCeleb1數據集的x向量基線，我們提出的方法在EER相對改進方面最多可存檔11.6％，7.9％和7.6％. | zh_TW |
| dc.description.abstract | In this thesis, we present a variant of GRU architecture called Dual-Sequences Gated Attention Unit (DS-GAU), in which the statistics pooling from each TDNN layer of the x-vector baseline are computed and passed through the DS-GAU layer, to aggregate more information from the variant temporal context of input features while training as frame-level. Our proposed architecture was trained on the VoxCeleb2 dataset, where the feature vector is referred to as a DSGAU-vector. We made our evaluation on the VoxCeleb1 dataset and the Speakers in the Wild (SITW) dataset and compared the experimental results with the x-vector baseline system. It showed that our proposed method archived up to 11.6%, 7.9%, and 7.6% in EER relative improvements over the x-vector baseline on the VoxCeleb1 dataset. | en_US |
| DC.subject | 應用於語者驗證之雙序列門控注意力單元架構 | zh_TW |
| DC.title | 應用於語者驗證之雙序列門控注意力單元架構 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Dual-Sequences Gated Attention Unit Architecture for Speaker Verification | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |