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Unsupervised Speaker Indexing of Discussions Using Anchor Models

机译:使用主持人模型的讨论的无监督发言人索引

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摘要

We present unsupervised speaker indexing, combined with automatic speech recognition (ASR) for speech archives, such as discussions. Our proposed indexing method is based on anchor models, by which we define a feature vector based on the similarity with speakers of a large-scale speech database. We introduce dimensional normalization and reduction on the vectors to improve discriminant ability. These vectors are then clustered and initial speaker labels are obtained. Using the initial labels, speaker models are constructed for respective clusters and the speakers are finally indexed with the speaker models. We perform ASR using the results of this indexing. We achieved a speaker indexing accuracy of 91% and a significant improvement in the ASR for real discussion data.
机译:我们介绍了无监督的说话者索引,并结合了语音存档(例如讨论)的自动语音识别(ASR)。我们提出的索引方法基于锚模型,通过锚模型,我们基于与大型语音数据库说话者的相似性来定义特征向量。我们在向量上引入尺寸归一化和归约以提高判别能力。然后将这些向量聚类,并获得初始说话者标签。使用初始标签,为各个群集构建扬声器模型,并最终用扬声器模型对扬声器进行索引。我们使用此索引结果执行ASR。我们的发言人索引准确度达到91%,并且在实际讨论数据方面的ASR有了显着提高。

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