Improving the noise robustness of automatic speech recognition systems has been a
challenging task for many years. Recently, it was found that Deep Neural Networks (DNNs) …

The performance of human listeners degrades rather slowly compared to machines in noisy
environments. This has been attributed to the ability of performing auditory scene analysis …

Recognition of Reverberant Speech by Missing Data Imputation and NMF Feature
Enhancement Page 1 Recognition of Reverberant Speech by Missing Data Imputation and …

A missing data mask estimation method based on Gaussian-Bernoulli restricted Boltzmann
machine (GRBM) trained on cross-correlation representation of the audio signal is …

In the sparse imputation approach, missing spectral components of speech are estimated
using the compressive sensing technique. For this purpose, a dictionary of clean speech …

While human listening is robust in complex auditory scenes, current speech segregation
algorithms do not perform well in noisy and reverberant environments. This paper addresses …

This paper presents a method for reconstructing unreliable spectral components of speech
signals using the statistical distributions of the clean components. Our goal is to model the …

Two main categories of speech recognition robustness through missing data are spectral
imputation and classifier modification. In this paper, we introduce a novel technique that …

From prehistory to the multimedia digital age, speech communication has been the
dominant mode of human social bonding and information exchange. With the advancement …

Spectral imputation and classifier modification can be counted as the two main missing data
approaches for robust automatic speech recognition (ASR). Despite their potentials, little …