SLHMM: a continuous speech recognition system based on Alphanet-HMM

Abstract

This paper presents a new framework developed to apply Alphanets to CSR. For this purpose, a modular system is proposed. This system is made up by three different modules: LVQ module, SLHMM module and DP module. The SLHMM module is an expansion of an Alphanet, and therefore, can be interpreted as a HMM. The system can be trained globally applying backpropagation techniques. The used pruning procedure is based upon recognized units instead of observations, which reduces the number of nodes needed to recognize a sentence, compared to HMM-based systems using the same parameters for the models in both systems. Besides, the training procedure re-adapts the weights according to the new architecture in a few iterations since the initial parameters can be estimated from a classical HMM CSR system.<>