Text-independent speaker identification based on selection of the most similar feature vectors

详细信息    查看全文

• 作者：Mohammad Soleymanpour ; Hossein Marvi
• 关键词：Speaker identification ; MFCC ; Feature selection ; Clustering ; Neural network
• 刊名：International Journal of Speech Technology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：20
• 期：1
• 页码：99-108
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing; Social Sciences, general; Artificial Intelligence (incl. Robotics);
• 出版者：Springer US
• ISSN：1572-8110
• 卷排序：20

文摘

The speaker recognition has been one of the interesting issues in signal and speech processing over the last few decades. Feature selection is one of the main parts of speaker recognition system which can improve the performance of the system. In this paper, we have proposed two methods to find MFCCs feature vectors with the highest similar that is applied to text independent speaker identification system. These feature vectors show individual properties of each person’s vocal tract that are mostly repeated. They are used to build speaker’s model and to specify decision boundary. We applied MFCC of each window over main signal as a feature vector and used clustering to obtain feature vectors with the highest similar. The Speaker identification experiments are performed using the ELSDSR database that consists of 22 speakers (12 male and 10 female) and Neural Network is used as a classifier. The effect of three main parameters have been considered in two proposed methods. Experimental results indicate that the performance of speaker identification system has been improved in accuracy and time consumption term.