دانلود مقاله انگلیسی رایگان:یادگیری عمیق چند متری برای تأیید گوینده مستقل از متن - 2020
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  • Deep multi-metric learning for text-independent speaker verification Deep multi-metric learning for text-independent speaker verification
    Deep multi-metric learning for text-independent speaker verification

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Deep multi-metric learning for text-independent speaker verification


    ترجمه فارسی عنوان مقاله:

    یادگیری عمیق چند متری برای تأیید گوینده مستقل از متن


    منبع:

    Sciencedirect - Elsevier - Neurocomputing, 410 (2020) 394-400. doi:10.1016/j.neucom.2020.06.045


    نویسنده:

    Jiwei Xu, Xinggang Wang⇑, Bin Feng, Wenyu Liu


    چکیده انگلیسی:

    Text-independent speaker verification is an important artificial intelligence problem that has a wide spectrum of applications, such as criminal investigation, payment certification, and interest-based customer services. The purpose of text-independent speaker verification is to determine whether two given uncontrolled utterances originate from the same speaker or not. Extracting speech features for each speaker using deep neural networks is a promising direction to explore and a straightforward solution is to train the discriminative feature extraction network by using a metric learning loss function. However, a single loss function often has certain limitations. Thus, we use deep multi-metric learning to address the problem and introduce three different losses for this problem, i.e., triplet loss, n-pair loss and angular loss. The three loss functions work in a cooperative way to train a feature extraction network equipped with Residual connections and squeeze-and-excitation attention. We conduct experiments on the large-scale VoxCeleb2 dataset, which contains over a million utterances from over 6; 000 speakers, and the proposed deep neural network obtains an equal error rate of 3:48%, which is a very competitive result. Codes for both training and testing and pretrained models are available at https://github.com/ GreatJiweix/DmmlTiSV, which is the first publicly available code repository for large-scale textindependent speaker verification with performance on par with the state-of-the-art systems.
    Keywords: Speaker verification | N-pair loss | Angular loss | triplet loss | SENet


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 7
    حجم فایل: 443 کیلوبایت

    قیمت: رایگان


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