Nonlinear noise compensation in feature domain for speech recognition with numerical methods

In this paper, we propose to compensate noise in the log-spectral domain for robust speech recognition based on a nonlinear environmental model. In our approach, starting from the original nonlinear speech distortion model in the feature domain, we derive the minimum mean square error (MMSE) estimation of clean speech signal given a noisy observation, which turns out to be an integral of a complex nonlinear function. In this work, we propose to use a numerical method to solve the above nonlinear integral. It requires higher computational complexity than the normal linear approximation methods but it is usually affordable since calculation is performed entirely in the pre-processing feature domain without involving any change in speech decoders. Experimental results show that the proposed nonlinear method outperforms the conventional vector Taylor series (VTS) method in terms of ASR performance when dealing with artificial white Gaussian noise as well as true hands-free noisy speech, especially in low SNR levels.