On the impact of using optimization search method in large speaker database based on hybrid modeling over experimental investigation

Abstract

Speaker recognition from speech signal is still an ongoing research in forensics and biometrics area. Speaker recognition is the process to enable machine to recognize speaker's identity from their speech. Recent development on classify speaker data from a group of speaker is still insufficient to provide a satisfied result in achieving high performance pattern classification engine. There are two main difficulties in this field: how to maintain accuracy rate under incremental amounts of training data and how to reduce the time processing in the case embedded systems need to consider about efficient and simplicity of calculation. Recently we have proposed three difference hybrid pattern classification approach for text independent speaker identification system; in these approaches, we combined a hybrid GMMNQ and decision Tree model. The aim of this paper is to show the progress of the development of a high impact hybrid modeling. Besides, via this paper, an evaluation is done to verify the impact of using optimization search method on large speaker database.