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政大機構典藏 > 研究中心 > 人工智慧跨域研究中心 > 會議論文 > Item 140.119/148844

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/148844

Title:	Toward Text-independent Cross-lingual Speaker Recognition Using English-Mandarin-Taiwanese Dataset
Authors:	吳怡潔;廖文宏 Wu, Yi-Chieh;Liao, Wen-Hung
Contributors:	AI中心
Keywords:	Speaker recognition;Acoustic features;Text- independent speaker identification;Cross-lingual dataset
Date:	2021-01
Issue Date:	2023-12-22 10:30:45 (UTC+8)
Abstract:	Over 40% of the world's population is bilingual. Existing speaker identification/verification systems, however, assume the same language type for both enrollment and recognition stages. In this work, we investigate the feasibility of employing multilingual speech for biometric applications. We establish a dataset containing audio recorded in English, Mandarin and Taiwanese. Three acoustic features, namely, i-vector, d-vector and x-vector have been evaluated for both speaker verification (SV) and identification (SI) tasks. Preliminary experimental results indicate that x-vector achieves the best overall performance. Additionally, the model trained with hybrid data demonstrates the highest accuracy, at the cost of extra data collection efforts. In SI tasks, we obtained over 91 % cross-lingual accuracy in all models using 3-second audio. In SV tasks, the EER among cross-lingual test is at most 6.52 %, which is observed on the model trained by English corpus. The outcome suggests the feasibility of adopting cross-lingual speech in building text-independent speaker recognition systems.
Relation:	2020 25th International Conference on Pattern Recognition, International Association for Pattern Recognition(IAPR)
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/ICPR48806.2021.9412170
DOI:	10.1109/ICPR48806.2021.9412170
Appears in Collections:	[人工智慧跨域研究中心] 會議論文 [資訊科學系] 會議論文

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