MODELING A CLASS POSTERIOR PROBABILITY OF CONTEXT DEPENDENT PHONEMES IN A SPEECH RECOGNITION SYSTEM

What is disclosed is a system and method for modelling a class posterior probability of context dependent phonemes in a speech recognition system. A representation network is trained by projecting a N-dimensional feature vector into G intermediate layers of nodes. At least some features are associated with a class label vector. A last intermediate layer ZG of the representation network is discretized to obtain a discretized layer {circumflex over (Z)}. Feature vector Q is obtained by randomly selecting V features from discretized layer {circumflex over (Z)}. Q is repeatedly hashed to obtain a vector Qf where Qf is an output of the fth hashing. An equivalent scalar representation is determined for each Qf. In a manner more fully disclosed herein, a posterior probability Pf is determined for each (x, b) pair based on the equivalent scalar representation of each respective Qf. The obtained posterior probabilities are used to improve classification accuracy in a speech recognition system.