GRENZE International Journal of Computer Theory and Engineering
Authors:
Shreegowri A.J, D.J Ravi
Volume:
3
Issue:
2
Grenze ID:
01.GIJCTE.3.2.5
Pages:
1-5
Abstract
In contrast to the minimum mean square error (MMSE)-based noise cancelation
techniques, we propose a method to enhance speech by means of finding a mapping function
between noisy signal and clean speech signals based on deep neural networks (DNNs). In
order to handle a wide verity range of additive noises in real-world scenarios, a large
number of training set that contains many possible combinations of speech and noise types,
is first designed. The DNN architecture is then employed as a nonlinear regression function
to ensure a powerful modeling capability. To further improve the DNN-based speech
enhancement system we proposed a technique called global variance equalization to remove
the over-smoothing problem of the regression model to improve the generalization
capability of DNNs. To further improve the quality of enhanced speech and generalization
capability of DNNs. First, equalization between the global variance (GV) of the enhanced
features and the reference clean speech features is proposed to alleviate the over- smoothing
issue in DNN- based speech enhancement system.