© 2015, IJCSE All Rights Reserved 101                               Review Paper Volume-3, Issue-8 E-ISSN: 2347-2693 Speaker Recognition System Techniques and Applications Sukhandeep Kaur 1* and Kanwalvir Singh Dhindsa 2 1*,2 Dept.of, CSE, BBSBEC FatehgarhSahib, Punjab Technical University, INDIA Received: Jul /09/2015 Revised: Jul/22/2015 Accepted: Aug/20/2015 Published: Aug/30/ 2015 Abstract- Speaker verification is feasible method of controlling access to computer and communication network. It is an automatic process that uses human voice characteristics obtained from a recorded speech signal, as the biometric measurements to verify claimed identity of speaker. It can be classified into two categories, text–dependent and text-independent system. This paper introduces the fundamental concepts of speaker verification for security system. It focuses on techniques and their unique features. Keywords- Speaker Identification, Gamma Tone Frequency Cepstral Coefficient, Mel Frequency Cepstral Coefficient 1. INTRODUCTION Speech is the primary way of communication between humans. Speaker recognition is the process of automatically recognizing an individual on the basis of characteristics of words spoken. Speaker recognition has always target on security system for managing the access to protected information from being used by anyone. Speaker verification is the branch of biometric authentication. This paper runs over comparison of voice recognition techniques. The parameter should be easily extracted, not be easily imitated, not to change with space and time as far as signal contains LCP, LPCC, MFCC, GFCCetc[4]. The current commonly used methods for speaker recognition are GMM (Gaussian Mixture Model) , HMM (Hidden Markov Model), ANN (Artificial Neural Network) etc. GMM extends of Gaussian probability density function working well in speaker recognition systems because of its capability to approximate the probability density distribution of arbitrary shape perfectly. HMM performs well in speaker recognition has a high accuracy. The three different methods based on HMM are DHMM, CHMM, and SCHMM [1]. ANN is a computational model based on the structure and functions of biological neural networks.ANN have three layers that are interconnected. The first layer consists of input neurons. Those neurons send data on to the second layer, which in turn sends the output neurons to the third layer. 2. RELATED WORK Mukherjee et al. [2]discussed voice is one of the most assure and develop biometric modalities for access control. This paper presents a new method to recognize speakers by involve a new set of characters and using Gaussian mixture models (GMMs). In this research, the method of shifted MFCC was introduced so as to incorporate accent information in the recognition algorithm. The algorithm is evaluated using TIDIGIT dataset and the results showed improvements. Wang and Ching[7]focussed on the features estimation method leads to robust recognition performance, specially at low signal-to-noise ratios. In the context of Gaussian mixture model-based speaker recognition with the presence of additive white Gaussian noise, the new approach produces logicalreduction of both recognition error rate and equal error rateat signal-to-noise ratios ranging from 0 to 15 db. Faraj and Bigun [8]presented the first extended study investigationthe added value of lip motion features for speaker and speech-recognition applications. Digit identification and person-recognition andconfirmation experiments were conducted on the publicly available XM2VTS database showing goodresults (speaker verification was 98 percent, speaker recognition was 100 percent, and digit identification was 83 percent to 100 percent). Sinith et al. [9]detailed the lay accent on text-Independent speaker recognition system where we adopted Mel- Frequency Cepstral Coefficients (MFCC) as the speaker speech feature argument in the system and the concept of Gaussian Mixture Modeling (GMM) for modeling the extracted speech feature. The Maximum likelihood ratio detector algorithm is used for the decision making process. The experimental study has been performed for various speeches time duration and several languages and was conducted around MATLAB 7 language environment. Gaussian mixture speaker model achieve high recognition rate for various speech durations.