Conduent Business Services, LLC

Niranjan Aniruddha Viladkar

Vivek Tyagi

Theja Tulabandhula

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 Zof 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 Qwhere Qis an output of the fhashing. An equivalent scalar representation is determined for each Q. In a manner more fully disclosed herein, a posterior probability Pis determined for each (x, b) pair based on the equivalent scalar representation of each respective Q. The obtained posterior probabilities are used to improve classification accuracy in a speech recognition system.

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Maharashtra, India

Niranjan Aniruddha Viladkar