Incorporating mask modelling for noise-robust automatic speech recognition

In this paper we investigate an incorporation of mask modelling into an HMM-based ASR system. The mask model is estimated for each HMM state and mixture by using a separate Viterbi-style training procedure and it expresses which regions of the spectrum are expected to be uncorrupted by noise for the HMM state. Experimental evaluation is performed on noisy speech data from the Aurora 2 database. Significant performance improvements are achieved when the mask modelling is incorporated within the standard model and two models that had already compensated for the effect of the noise.