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題名:	Toward Text-independent Cross-lingual Speaker Recognition Using English-Mandarin-Taiwanese Dataset
作者:	吳怡潔;廖文宏 Wu, Yi-Chieh;Liao, Wen-Hung
貢獻者:	AI中心
關鍵詞:	Speaker recognition;Acoustic features;Text- independent speaker identification;Cross-lingual dataset
日期:	2021-01
上傳時間:	2023-12-22 10:30:45 (UTC+8)
摘要:	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.
關聯:	2020 25th International Conference on Pattern Recognition, International Association for Pattern Recognition(IAPR)
資料類型:	conference
DOI 連結:	https://doi.org/10.1109/ICPR48806.2021.9412170
DOI:	10.1109/ICPR48806.2021.9412170
顯示於類別:	[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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