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 activa-tion 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