Multilayer perceptrons for state-dependent weightings of HMM likelihoods

This paper proposes multi-layer perceptrons (MLPs) use in state-dependent weightings of Hidden Markov Model (HMM) likelihoods. The static pattern classification ability of MLPs and the temporal processing capability of HMMs are employed in order to obtain the state-dependent weightings of HMM likelihoods. In this approach, the MLP is trained for phoneme classification, and then the output values of the MLP are used as the state-dependent weightings. Applying the MLP outputs to the state-dependent weightings improves the performance of the conventional HMM without state-dependent weightings. However, in order to further improve the discriminability of competing classes, the discriminative training of the state-dependent weightings is performed by computing the gradient of the optimization criterion for the state-weighted HMM with respect to the MLP parameters. The proposed algorithm reduces the error rate considerably as compared with the conventional HMM in speaker-independent continuous speech recognition.