Fusion of multiple uncertainty estimators and propagators for noise robust ASR

Uncertainty decoding has been successfully used for speech recognition in highly nonstationary noise environments. Yet, accurate estimation of the uncertainty on the denoised signals and propagation to the features remain difficult. In this work, we propose to fuse the uncertainty estimates obtained from different uncertainty estimators and propagators by linear combination. The fusion coefficients are optimized by minimizing a measure of divergence with oracle estimates on development data. Using the Kullback-Leibler divergence, we obtain 18% relative error rate reduction on the 2nd CHiME Challenge with respect to conventional decoding, that is about twice as much as the reduction achieved by the best single uncertainty estimator and propagator.