Generic Modeling Applied to Speaker Count

A. N. Iyer, U. Ofoegbu, R. Yantorno, B. Y. Smolenski
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引用次数: 6

Abstract

The problem of determining the number of speakers participating in a conversation and building their models in short conversations, within an unknown group of speakers, is addressed in this paper. The lack of information about the number of speakers and the unavailability of sufficient data present a challenging task of efficiently estimating the speaker model parameters. The proposed method uses a novel generic speaker identification (GSID) system as a guide in the model building process. The GSID system is designed performing speaker identification where the speaker associated with the test data may not be enrolled. The models in the GSID system are employed as initial speaker models, representing the persons participating in the conversation, and are subjected to a classification-adaptation procedure. The classification is performed based on the Bhattacharyya distance between the model database and the test data being analyzed. The model database of the system is designed to consist of simple and well separated models. A technique to generate such generic models is introduced. The proposed method was applied to the speaker count problem and has produced an overall accuracy of 75.3% in determining if there were 1, 2 or 3 speakers in a conversation
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应用于说话人计数的通用建模
本文解决了在未知的说话者群体中确定参与对话的说话者数量并在简短对话中建立他们的模型的问题。由于扬声器数量信息的缺乏和数据的缺乏,对扬声器模型参数的有效估计提出了一项具有挑战性的任务。该方法采用一种新的通用说话人识别系统(GSID)作为模型构建过程的指导。GSID系统的设计是在与测试数据相关的扬声器可能未被注册的情况下执行扬声器识别。GSID系统中的模型被用作初始说话人模型,代表参与对话的人,并经过分类适应过程。基于模型数据库与待分析测试数据之间的Bhattacharyya距离进行分类。系统的模型数据库被设计成由简单且分离良好的模型组成。介绍了一种生成这种通用模型的技术。所提出的方法被应用于说话人数量问题,并在确定对话中是否有1、2或3个说话人方面产生了75.3%的总体准确率
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