9. A Hidden Markov Model based speech recognition system comprising: a representation network having an input layer of N nodes and G≥2 intermediate layers of nodes, each intermediate layer having N g nodes, where g ∈(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; and at least one processor executing machine readable program instructions which configure the processor to: receive 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; initialize the representation network by assigning a feature to each node of the input layer; train the representation network by computing, for all nodes of each intermediate layer, discriminative features based on features of nodes in a preceding layer; compute a posterior probability P f for each (x, b) pair comprising: discretize features of last intermediate layer Z G into E-intervals to obtain a discretized layer {circumflex over (Z)}, where E≥2; generate a feature vector Q=(q 1 , q 2 , . . . , q V ) by randomly selecting V ∈(1, N G ) features from discretized layer {circumflex over (Z)}; for f=1 to F, hash 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; determine an equivalent scalar representation for each Q f ; and for f=1 to F, determine a posterior probability P f for each (x, b) pair based on the respective equivalent scalar representation; use the posterior probabilities to improve classification accuracy of context dependent phonemes in the Hidden Markov Model based speech recognition system; and use the Hidden Markov Model based speech recognition system to recognize speech.
15. The speech recognition system of claim 9 , wherein the representation network is configured dynamically based on a dimensionality of the received feature vector.