Concurrent Neural Networks for Speaker Recognition

We propose a new recognition model called Concurrent Neural Networks (CNN), representing a winner-takes-all collection of neural networks. Each network of the system is trained individually to provide best results for one class only. We have applied the above model for the task of speaker recognition. We performed distinct speaker recognition experiments using three variants of basic components of the CNN system: the Multi-Layer Perceptron (MLP), the TimeDelay Neural Network (TDNN) and the Kohonen Self-Organizing Map (SOM). We have used two databases: a clean speech database called SPEECHDATA and a telephone database called TELEPHDATA. The experiments proved a significant increase of the recognition score using the proposed CNN model by comparation to the use of a single neural network for the whole speaker recognition task. The SOM best has performed in our experiments proving an increase of about 38% for SPEECHDATA as well as an increase of about 30% for TELEPHDATA.