An analysis of vector Taylor series model compensation for non-stationary noise in speech recognition

In this paper, we investigate a feature conditioning method for the VTS-based model compensation. The VTS is a technique that predicts noisy acoustic model from clean acoustic model and noise model. It is noted that most of the previous studies use a single Gaussian noise model, which is unable to model noise statistics well, especially in non-stationary noisy environments. In this paper, we propose a combination of feature processing and VTS model compensation to handle non-stationary noise more efficiently. In the feature processing stage, the non-stationary characteristics of noise is reduced, hence the processed features is more suitable for VTS model compensation using single Gaussian noise model. Experimental analysis on the AURORA2 task shows that the proposed method has the potential to improve the performance of VTS method in non-stationary environments if good noise estimation is available.