A Technique to Overcome the Problem of Small Size Database for Automatic Speaker Recognition

Abstract

Modeling a system by statistical methods needs large amount of data to train the system. In real life such data are sometimes not available or hard to collect. Modeling the system with small size database will produce a system with poor performance. In this paper we propose a method for increasing the size of the database. The method works by generating new samples from the original samples, using combinations of the following methods: speech lengthening, noise adding, and word reversal. To make a proof of concept, we used a severe test condition, in which the original database consists of one sample per speaker, for a speaker recognition system. We tested the system using original samples. The best results were 90% and 90.41% recognition rates for two subsets of the database for 25 and 50 speakers respectively.