Probabilistic optimum filtering for robust speech recognition

We present a new mapping algorithm for speech recognition that relates the features of simultaneous recordings of clean and noisy speech. The model is a piecewise linear transformation applied to the noisy speech feature. The transformation is a set of multidimensional linear least-squares filters whose outputs are combined using a conditional Gaussian model. The algorithm was tested using SRI's DECIPHER speech recognition system. Experimental results show how the mapping is used to reduce recognition errors when the training and testing acoustic environments do not match.< >