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This paper proposes an effective noise estimation method for speech enhancement to improve speech recognition in time-varying noise conditions. The proposed noise estimation scheme employs the Variation Model Composition (VMC) method. The VMC method generates multiple noise models by selectively applying perturbation factors to the mean parameters of a basis noise model. The resulting collection of the noise models is expected to effectively reflect the unseen noise signal included in the speech segments. The obtained noise models are used to generate multiple environmental models employing the parallel model combination method. The noise estimate is obtained by using the posterior probability of the multiple environmental models. The proposed noise estimation method is applied to the Spectral Subtraction. The proposed speech enhancement scheme is evaluated within the Aurora 2.0 evaluation framework over speech babble and background music noise conditions. Experimental results demonstrate that the proposed method is effective at increasing speech recognition accuracy in time-varying background noise conditions. 1122 Sunmee Kang and Wooil Kim

Noise estimation employing variational model composition for speech enhancement in time-varying noise conditions