Performance of Speaker Recognition System Using Kernel Functions Approach for Different Noise Levels Renu Singh, Arvind Kumar Singh, and Utpal Bhattacharjee Abstract Speaker recognition is one of the most popular voices biometric tech- nique used for security reason in many areas like banking system, online-shopping, database access etc. The recognition performance of speaker recognition system is very satisfactory in noise-free environments, whereas the improved performance in case of low level signal-to-noise ratio (SNR) is the need in the present days. Hence, in this study speaker recognition performance has been evaluated at different signal- to-noise ratios using SVM-based various kernel functions approach and principal component analysis (PCA). The proposed scheme has been applied on NIST 2003 and AURORA dataset and found that the recognition accuracy and running time improves at low level SNRs using kernel functions. Keywords Speaker recognition system · SVM · Principle component analysis (PCA) · Signal-to-noise ratio 1 Introduction Speaker recognition is a method of recognizing a speaker’s voice by utilizing explicit information comprised in speech waves [1, 2].This methodology can be used to substantiate the characteristics claimed by the person who wants to access the systems i.e. enabling the access control of different services with the help of voice. The various applications of speaker recognition system are voice dialing, phone banking, shop- ping over phone, database access services, reservation services, services on voice mail R. Singh (B ) · A. K. Singh Department of Electrical Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli, Arunachal Pradesh 791109, India e-mail: renumona08@gmail.com A. K. Singh e-mail: singharvindk67@gmail.com U. Bhattacharjee Department of Computer Science, Rajiv Gandhi University, Itanagar, Arunachal Pradesh 791112, India e-mail: utpalbhattacharjee@rediffmail.com © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 P. K. Mallick et al. (eds.), Electronic Systems and Intelligent Computing, Lecture Notes in Electrical Engineering 686, https://doi.org/10.1007/978-981-15-7031-5_49 513