Speaker identification using neural tree networks

A modified neural tree network (NTN) is examined for use in text independent speaker identification. The NTN is a hierarchical classifier that combines the properties of decision trees and feed-forward neural networks. The modified NTN uses discriminant learning to partition feature space as opposed to the more common clustering approaches, such as vector quantization. The modified NTN also uses forward pruning to avoid overfitting the training data. The modified NTN is evaluated for both closed and open set speaker identification experiments using the TIMIT database. The performance of the modified NTN is compared to that of vector quantization classifiers. The results presented show the modified NTN to provide comparable performance to the vector quantization classifier for closed set speaker identification while providing improved performance for the open set problem