Discriminant Initialization for Factor Analyzed HMM Training

Factor analysis has been recently used to model the covariance of the feature vector in speech recognition systems. Maximum likelihood estimation of the parameters of factor analyzed HMMs (FAHMMs) is usually done via the EM algorithm, meaning that initial estimates of the model parameters is a key issue. In this paper we report on experiments showing some evidence that the use of a discriminative criterion to initialize the FAHMM maximum likelihood parameter estimation can be effective. The proposed approach relies on the estimation of a discriminant linear transformation to provide initial values for the factor loading matrices, as well as appropriate initializations for the other model parameters. Speech recognition experiments were carried out on the Wall Street Journal LVCSR task with a 65k vocabulary. Contrastive results are reported with various model sizes using discriminant and non discriminant initialization