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首页> 外文会议>Annual conference of the International Speech Communication Association;INTERSPEECH 2011 >Speaker verification robust to talking style variation using multiple kernel learning based on conditional entropy minimization
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Speaker verification robust to talking style variation using multiple kernel learning based on conditional entropy minimization

机译：基于条件熵最小化的多核学习对说话风格变化具有鲁棒性的说话人验证

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We developed a new speaker verification system that is robust to intra-speaker variation. There is a strong likelihood that intra-speaker variations will occur due to changes in talking styles, the periods when an individual speaks, and so on. It is well known that such variation generally degrades the performance of speaker verification systems. To solve this problem, we applied multiple kernel learning (MKL) based on conditional entropy minimization, which impose the data to be compactly aggregated for each speaker class and ensure that the different speaker classes were far apart from each other. Experimental results showed that the proposed speaker verification system achieved a robust performance to intra-speaker variation derived from changes in the talking styles compared to the conventional maximum margin-based system.
机译：我们开发了一种新的说话人验证系统，该系统可对说话人内部变化产生鲁棒性。由于说话风格，个人说话时间等的变化，很有可能发生说话人内部差异。众所周知，这种变化通常会降低说话者验证系统的性能。为了解决这个问题，我们基于条件熵最小化应用了多核学习（MKL），该算法将每个说话者类别的数据紧凑地汇总在一起，并确保不同的说话者类别彼此分开。实验结果表明，与传统的基于最大余量的系统相比，所提出的说话人验证系统对说话人风格变化引起的说话人内部说话人变化具有鲁棒的性能。

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