Paper Title
Feature Extraction For Speaker Recognition

Identification of the people in today’s world is important than ever. We have many bio-metric methods to so this like fingerprint, face recognition, retina scan etc. these techniques are capable of identifying only one person at a time. If bio-metric information of two or more people is mixed together then these methods fail catastrophically. Also in these methods require the user to be in a specific position for a specified time, which can get very tiresome and hectic if large number of people are to be identified. In this paper we focus on the identification of people based on speech. Also we deal with the problem of multiple people taking a single instant. We base our research of project on text-dependent model and then we even try to analysis the text-independent identification. First, speaker signal will go to pre-treatment process, where it will remove the background noise if any and required. Then, features from speech signal will be extracted using Cepstrum domain. We get very promising results even at moderate noise levels. The resemblance of features used for database and for testing is good. Index Terms—Bio-metric, Cepstrum, text-dependent model, text-independent model.