Degree Granting Department
Ravi Sankar, Ph.D.
Wilfrido Moreno, Ph.D.
Paris Wiley, Ph.D.
Fourier Transform, Accent Modeling, Speech Processing, MFCC, Gaussian Mixture Model
Speaker Recognition is the art of recognizing a speaker from a given database using speech as the only input. In this thesis we will be discussing a novel approach to detect speakers. Here we will introduce the concept of shifted MFCC to add improvement over the performance from previous work which has shown quite a decent amount of accuracy of about 95% at best. We will be talking about adding different parameters which also contributed in improving the efficiency of speaker recognition. Also we will be testing our algorithm on Text dependent speech data and Text Independent speech data. Our technique was evaluated on TIDIGIT - database. In order to further increase the speaker recognition rate at lower FARs, we combined accent information added with pitch and higher order formants. The possible application areas for the work done here is in any access control entry system or now a day's a lot of smart phones, laptops, operating systems etc have Also, in homeland security applications; speaker accent will play a critical role in the evaluation of biometric systems since users will be international in nature. So incorporating accent information into the speaker recognition/verification system is a key component that our study focused on. The accent incorporation method and Shifted MFCC techniques discussed in this work can also be applied to any other speaker recognition systems.
Scholar Commons Citation
Mukherjee, Rishiraj, "Speaker Recognition Using Shifted MFCC" (2012). USF Tampa Graduate Theses and Dissertations.