Bayesian feature enhancement using a mixture of unscented transformation for uncertainty decoding of noisy speech

A new parameter estimation method for the model-Based feature enhancement (MBFE) is presented. The conventional MBFE uses the vector Taylor series to calculate the parameters of non-linearly transformed distributions, though the linearization leads to a degraded performance. We use the unscented transformation to estimate the parameters, where a minimal number of samples propagated through the nonlinear transformation are used. By avoiding the linearization, the parameters are estimated more accurately. Experimental results on Aurora2 show that the proposed method reduces the word error rate by 8.48% relatively, while the computational cost is just modestly higher, compared with the conventional MBFE.