Noisy speech recognition using variance adapted likelihood measure

Because the norm of testing cepstral vector was shrinked in a noisy environment, the model parameters, i.e., mean vector and covariance matrix, should be adapted simultaneously. We propose a method called variance adapted likelihood measure (VALM) which adapts the mean vector using a projection-based scale factor and adapts the covariance matrix using a variance reduction function estimated from the training database. The variance reduction function can be obtained according to various phonetic units. In the hidden Markov model based experiments, the speech recognition performance is greatly improved by applying VALM. The most significant improvement is achieved when the variance reduction function is separately estimated for different state parameters