Patent Document ID: 20180047388
Application ID: 15233582
Patent Flag: 0

Claim One:
1. A computer implemented method for modeling a class posterior probability of context dependent phonemes in a speech recognition system, the computer implemented method comprising: receiving a N-dimensional feature vector x 1 , x 2 ,. .. , x N with a respective M-dimensional class label vector b 1 , b 2 ,. .. , b M , where (x, b) represents the x th feature and its label; assigning a feature to each node of the input layer of a representation network comprising an input layer of N nodes and G≧2 intermediate layers of nodes with each intermediate layer having N g nodes, where gε(1, G), all edges of all nodes of the input layer being connected to all nodes of a first intermediate layer, all edges of all nodes of each intermediate layer being connected to all nodes of a next successive intermediate layer; computing, for all nodes of each intermediate layer, discriminative features based on features of nodes in a preceding layer; discretizing features of last intermediate layer Z G into E-intervals to obtain a discretized layer {circumflex over (Z)}, where E≧2; generating a feature vector Q=(q 1 , q 2 ,. .. , q V ) by randomly selecting Vε(1, N G ) features from discretized layer {circumflex over (Z)}; for f=1 to F, hashing Q into Q f =(q 1 f , q 2 f ,. .. , q V f ), where Q f is obtained from the f th hashing, where F≧2; determining an equivalent scalar representation for each Q f ; for f=1 to F, determining a posterior probability P f for each (x, b) pair based on the determined respective equivalent scalar representation; and use the posterior probabilities to improve classification accuracy in a speech recognition system.