Context dependent state tying for speech recognition using deep neural network acoustic models

This paper proposes an algorithm to design a tied-state inventory for a context dependent, neural network-based acoustic model for speech recognition. Rather than relying on a GMM/HMM system that operates on a different feature space and is of a different model family, the proposed algorithm optimizes state tying on the activation vectors of the neural network directly. Experiments show the viability of the proposed algorithm reducing the WER from 36.3% for a context independent system to 16.0% for a 15000 tied-state system.