Robust Feature Compensation in Nonstationary and Multiple Noise Environments

Abstract

The probabilistic optimum filtering (POF) algorithm is a piece wise linear transformation of the noisy speech feature space into the clean speech feature space. In this work we extend the POF algorithm to allow a more accurate way to select noisy-to-clean feature mappings, by allowing different combinations of speech and noise to have combination-specific mappings selected depending on the observation. This is especially important in nonstationary environments, where different noise segments will result in different observations in the noisy feature space. Experimental results using stationary and nonstationary noises show the effectiveness of the proposed technique compared to the old approach. We also explored the use of the extended POF method to train a map with multiple noises in order to gain generalization over different noise types and be able to tackle unknown noise environments.