期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2016
卷号：10
期号：1
页码：279-286
DOI：10.14257/ijseia.2016.10.1.27
出版社：SERSC
摘要：This paper proposes a new feature vector selection method for voice pattern recognition tasks, especially for speaker or emotion recognition. During the model training phase, robust speaker or emotion models are constructed by using meaningful feature vectors while discarding confusing vectors that may induce recognition error. To select meaningful feature vectors, the proposed method classifies feature vectors into overlapped and non-overlapped sets using log-likelihood ratio. Speaker- and emotion- recognition experiments confirmed that these robust models significantly reduce recognition errors.
