Speech recognition in noise using a projection-based likelihood measure for mixture density HMM´s

In this study, a cepstral likelihood measure based on the projection operation is incorporated into a mixture density hidden Markov model (HMM) scheme to improve recognition in the presence of additive noise. The case in which the models are determined only under noise-free conditions is addressed. A background discussion and a derivation of the measure are provided. Recognition experiments are presented showing the usefulness of the proposed measure over the standard Gaussian measure (weighted Euclidean distance) for speaker independent, isolated word recognition in noise. It was found that the proposed mixture weighted projection measure significantly improved performance in several noise types, including white, jittering white, and colored noise. As an example, at an SNR of 10-dB white noise, recognition improved from only 38.4% correct using the Gaussian measure to 83.6% using the developed measure