Uncertainty Measures for Improving Exemplar-Based Source Separation

This work studies the use of observation uncertainty measures for improving the speech recognition performance of an exemplar-based source separation based front end. To generate the observation uncertainty estimates for the enhanced features, we propose the use of heuristic methods based on the sparse representation of the noisy signal in the exemplar-based source separation algorithm. The effectiveness of the proposed measures is evaluated in a large vocabulary noisy speech recognition task. The best proposed measure achieved relative error reductions up to 18 % over the baseline feature enhancement method without uncertainty measures.