Performance Evaluation of Speaker-Identification Systems for Singing Voice Data

Wei-Ho Tsai, Hsin-Chieh Lee
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Abstract

Automatic speaker-identification (SID) has long been an important research topic. It is aimed at identifying who among a set of enrolled persons spoke a given utterance. This study extends the conventional SID problem to examining if an SID system trained using speech data can identify the singing voices of the enrolled persons. Our experiment found that a standard SID system fails to identify most singing data, due to the significant differences between singing and speaking for a majority of people. In order for an SID system to handle both speech and singing data, we examine the feasibility of using model-adaptation strategy to enhance the generalization of a standard SID. Our experiments show that a majority of the singing clips can be correctly identified after adapting speech-derived voice models with some singing data.
歌唱语音数据的说话人识别系统性能评价
自动说话人识别(SID)一直是一个重要的研究课题。它的目的是确定在一组登记的人中谁说了给定的话语。本研究将传统的SID问题扩展到检查使用语音数据训练的SID系统是否可以识别入组人员的歌声。我们的实验发现,标准的SID系统无法识别大多数唱歌数据,因为大多数人在唱歌和说话之间存在显著差异。为了使SID系统同时处理语音和歌唱数据,我们研究了使用模型自适应策略来增强标准SID泛化的可行性。我们的实验表明,在使用一些歌唱数据调整语音衍生的语音模型后,可以正确识别大多数歌唱片段。
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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