Experiments in language identification with neural networks

Abstract

Automatic language identification is a computer-based process to determine the language of speakers while they are speaking. The authors have studied many types of conventional speech processing features and parametric and non-parametric models for classification. They have experimented with the restricted Coloumb energy (RCE) network and found that histograms of the RCE cell activations provide a good metric to identify languages. Results compare favourably to experiments reported by other researchers which have been duplicated. These experiments are discussed, and the databases which are currently being used in this research are described.<>