A mean vector compensation technique based on the projection-based group delay scheme has been combined with a semicontinuous HMM to improve the recognition rate in noisy environments. The proposed approach compensates the mean vector using a projection-based scale factor and the bias estimated From the training and/or testing data to balance the mismatch between different environments. Experiments show that the significant improvement in speaker-dependent, isolated word recognition was achieved by adding the projection-based scale factor and mean vector bias.
