Noise-dependent Gaussian mixture classifiers for robust rejection decision

Speech or speaker recognizers need to make a decision on either accepting or rejecting a recognized item, based on some measurement (e.g., likelihood) associated to the item. Distribution-based classification can be used to make the decision. In practical applications, the background noise level may adversely affect the distributions of the likelihoods and cause classification failure. A new decision mechanism is described, which treats the likelihoods as outcome of multidimensional Gaussian distributions with noise-dependent mean and covariance. The dependence on the noise is explicitly modeled as a polynomial function of noise level. The steps of estimating the decision parameters using the EM algorithm are given. Experimental results on in-car speech data show that the procedure, for noise ranging from a parked car (/spl sim/30 dB SNR) to highway (/spl sim/0 dB SNR) driving conditions, maintains a well-balanced decision performance between false rejection and false acceptance.