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首页> 外文期刊>IEICE Transactions on Information and Systems >An Unsupervised Speaker Adaptation Method for Lecture-Style Spontaneous Speech Recognition Using Multiple Recognition Systems
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An Unsupervised Speaker Adaptation Method for Lecture-Style Spontaneous Speech Recognition Using Multiple Recognition Systems

机译：基于多重识别系统的演讲风格自发语音识别的无监督说话人自适应方法

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摘要

This paper describes an accurate unsupervised speaker adaptation method for lecture style spontaneous speech recognition using multiple LVCSR systems. In an unsupervised speaker adaptation framework, the improvement of recognition performance by adapting acoustic models remarkably depends on the accuracy of labels such as phonemes and syllables. Therefore, extraction of the adaptation data guided by confidence measure is effective for unsupervised adaptation. In this paper, we looked for the high confidence portions based on the agreement between two LVCSR systems, adapted acoustic models using the portions attached with high accurate labels, and then improved the recognition accuracy. We applied our method to the Corpus of Spontaneous Japanese (CSJ) and the method improved the recognition rate by about 2.1 % in comparison with a traditional method.
机译：本文介绍了一种使用多个LVCSR系统进行演讲风格自发语音识别的准确无监督说话人自适应方法。在无人监督的说话人适应框架中，通过适应声学模型来提高识别性能的过程明显取决于诸如音素和音节等标签的准确性。因此，以置信度为指导的适应数据的提取对于无监督适应是有效的。在本文中，我们基于两个LVCSR系统之间的协议寻找高置信度部分，使用贴有高精度标签的部分改编声学模型，然后提高识别精度。我们将该方法应用于自发日语语料库（CSJ），与传统方法相比，该方法将识别率提高了约2.1％。

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