Microphone-Independent Robust Signal Processing Using Probabilistic Optimum Filtering.

摘要

A new mapping algorithm for speech recognition relates the features of simultaneous recordings of clean and noisy speech. The model is a piecewise nonlinear 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(Trademark) 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.