Using Neural Network with Speaker Applications

• Main Author: Baghdad Science Journal
• Format: Article
• Language: Arabic
• Published: College of Science for Women, University of Baghdad 2010-06-01
• Series: Baghdad Science Journal
• Subjects: "Speaker recognition, data enhancement, MLP"
• Online Access: http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/1074

In Automatic Speech Recognition (ASR) the non-linear data projection provided by a one hidden layer Multilayer Perceptron (MLP), trained to recognize phonemes, and has previous experiments to provide feature enhancement substantially increased ASR performance, especially in noise. Previous attempts to apply an analogous approach to speaker identification have not succeeded in improving performance, except by combining MLP processed features with other features. We present test results for the TIMIT database which show that the advantage of MLP preprocessing for open set speaker identification increases with the number of speakers used to train the MLP and that improved identification is obtained as this number increases beyond sixty. We also present a method for selecting the speakers used for MLP training which further improves identification performance.