A neural tree network for phoneme classification with experiments on the TIMIT database

Neural tree networks (NTNs) provide an efficient technique for pattern classification. They combine the concept of decision trees with neural networks (NNs). An efficient algorithm is presented for growing NTNs through analysis of the relative confusion among the classes. The NTN is tested on 36 phonemes extracted from the TIMIT database. Results show that this implementation with five hidden neurons at each tree node grows to 8 levels and scores 58.6% correct classification, as opposed to a NN with best performance of 52.4% using 130 hidden neurons. In addition to advantages in computational complexity and recognition performance, the NTN is found to provide important phonemic correlations which are known to exist in the human auditory system