, X t+τ are feature frames within a length 2τ + 1 window of features centered at time t. After several empirical trials, we decided on neural networks for vowel classification because of the availability of efficient discriminative training algorithms and their computational simplicity.
we use a simple 2-layer multi-layer perceptron (Bishop, 1995) whose input layer consists of 26 * 7 = 182 nodes, where 26 is the dimension of X t , the MFCC feature vector, and 2τ + 1 = 7 is the number of consecutive frames, and that has 50 hidden nodes (the numbers 7 and 50 were determined empirically).