Discriminative weighting of HMM state-likelihoods using the GPD method

We propose a new method of finding discriminative state-weights recursively using the generalized probabilistic descent method. This method is implemented with minor modification of the conventional parameter estimation and recognition algorithms by constraining the sum of the state-weights to the number of states in a recognition unit, and can be applied to continuous speech recognition as well as isolated word recognition. We confirm the validity of the method with phoneme-based and word-based state-weighting schemes for three kinds of recognition tasks.