Speaker Change Detection Based on Spectral Peak Track Analysis for Korean Broadcast News

2005 5th International Conference on Information Communications & Signal Processing Pub Date : 2005-12-06 DOI:10.1109/ICICS.2005.1689143

Ji-Soo Keum, Hyon-Soo Lee

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引用次数: 4

Abstract

In this paper, we propose a new speaker change detection algorithm based on spectral peak track analysis. When we pronounce the Korean sentence or words, it has a rhythm and intonation pattern varies according to the kinds of sentence and speaking style. Spectral peak track has relevance to this information and audio data type. Therefore, we use spectral peak track information as a speaker feature for change detection. The proposed method consists of three parts: data segmentation, feature generation based on spectral peak tracks analysis and change detection. We assume that the changing point exists at the breathing point, because these locations rely on sentence duration, speaking speed and style, and grammar. To evaluate the proposed method, we calculated the precision (PRC) and recall (RCL) for Korean broadcast news and compared these results with the BIC method for randomly selected segments. Experiment result, the PRC is 73.14% and the RCL is 85.46% for Korean broadcast news, and we have achieved a performance comparable to BIC

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基于谱峰跟踪分析的韩语广播新闻说话人变化检测

本文提出了一种基于频谱峰迹分析的说话人变化检测算法。当我们发音韩语句子或单词时，它有一个节奏和语调模式，根据句子的种类和说话风格而变化。谱峰轨迹与此信息和音频数据类型相关。因此，我们使用光谱峰轨迹信息作为说话人特征进行变化检测。该方法包括三个部分:数据分割、基于谱峰轨迹分析的特征生成和变化检测。我们假设变化点存在于呼吸点，因为这些位置依赖于句子的持续时间、说话速度和风格以及语法。为了评估所提出的方法，我们计算了韩国广播新闻的精确度(PRC)和召回率(RCL)，并将这些结果与随机选择片段的BIC方法进行了比较。实验结果显示，韩文广播新闻的PRC为73.14%，RCL为85.46%，达到了与BIC相当的性能

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2005 5th International Conference on Information Communications & Signal Processing

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