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Noise robustness has long garnered much interest from researchers and practitioners of the automatic speech recognition (ASR) community due to its paramount importance to the success of ASR systems. This paper presents a novel approach to improving the noise robustness of speech features, building on top of the dictionary learning paradigm. To this end, we employ the K-SVD method and its variants...
The purpose of this paper is to develop a novel speech feature extraction framework for independently compensating the real and imaginary acoustic spectra of speech signals in the modulation domain with the techniques of histogram equalization (HEQ) and non-negative matrix factorization (NMF). By doing so, we can enhance not only the magnitude but also the phase components of the acoustic spectra,...
In this paper, we propose a speech enhancement technique which compensates for the real and imaginary acoustic spectrograms separately. This technique leverages principal component analysis (PCA) to highlight the clean speech components of the modulation spectra for noise-corrupted acoustic spectrograms. By doing so, we can enhance not only the magnitude but also the phase portions of the complex-valued...
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