An HMM-based formalism for automatic subword unit derivation and pronunciation generation

We propose a novel hidden Markov model (HMM) formalism for automatic derivation of subword units and pronunciation generation using only transcribed speech data. In this approach, the subword units are derived from the clustered context-dependent units in a grapheme based system using maximum-likelihood criterion. The subword unit based pronunciations are then learned in the framework of Kullback-Leibler divergence based HMM. The automatic speech recognition (ASR) experiments on WSJ0 English corpus show that the approach leads to 12.7% relative reduction in word error rate compared to grapheme-based system. Our approach can be beneficial in reducing the need for expert knowledge in development of ASR as well as text-to-speech systems.