Vector Taylor Series based Joint

Joint uncertainty decoding has recently achieved promising results by using front-end uncertainty in the back-end in a mathematically consistent framework. One drawback of the method is that it relies on stereo-data or numerical algorithms, such as DPMC, which have high computational complexity and are difficult to deploy in real applications. We propose a Vector Taylor Series (VTS) approach to joint uncertainty decoding which provides a closed-form solution to the key problem of estimating the clean/noisy speech cross-covariance matrix. Our solution does not require stereo-data or numerical integration. We also propose a new strategy to deal with the cross-covariance matrix singularity. Experiments on Aurora2 show that VTS-based joint uncertainty decoding has similar accuracy compared to DPMC-based joint uncertainty decoding while being at least three times faster. Finally, VTS-based joint uncertainty decoding provided more than 2% absolute improvement when combined with our new strategy for cross-covariance singularity.