Task independent minimum confusibility training for continuous speech recognition

In this paper, a task independent discriminative training framework for subword units based continuous speech recognition is presented. Instead of aiming at the optimisation of any task independent figure, say the phone classification or recognition rates, we focus our attention to the reduction of the number of errors committed by the system when a task is defined. This consideration leads to the use of a segmental approach based on the minimisation of the confusibility over short chains of subword units. Using this framework, a reduction of 32% in the string error rate may be achieved in the recognition of unknown length digit strings using task independent phone like units.