Investigations into the incorporation of the Ideal Binary Mask in ASR

While much work has been dedicated to exploring how best to incorporate the Ideal Binary Mask (IBM) in automatic speech recognition (ASR) for noisy signals, we demonstrate that the simple use of masked speech can outperform standard spectral reconstruction methods. We explore the effects of both the accuracy of the mask estimation and the strength of the language model on our results. The relative performance of these techniques is directly tied to the accuracy of the estimated mask. Although the use of masked speech fails when significant numbers of errors are present, the maximum performance for spectral reconstruction techniques also drops significantly. This implies improvements in mask estimation can provide greater gains in ASR performance than improvements in the incorporation of the IBM in ASR. Previous work may have ignored the direct use of masked speech due to its poor performance on tasks without a strong language model.