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Sub-band speech processing is well-known in robust speech recognition. On the other hand, in recent years, deep neural networks (DNNs) have been widely used in speech recognition for acoustic modeling and also feature extraction and transformation. In this paper, we propose to use deep belief network (DBN) as a post-processing method for de-noising in Mel sub-band level where we enhance logarithm...
Keyword spotting refers to detection of all occurrences of any given word in a speech utterance. In this paper, we define the keyword spotting problem as a binary classification problem and propose a discriminative approach for solving it. Our approach exploits evolutionary algorithm to determine the separating hyper plane between two classes: class of sentences containing the target keywords and...
Feature extraction is an important step in pattern classification and speech recognition. Extracted features should discriminate classes from each other while being robust to the environmental conditions such as noise. For this purpose, some transformations are applied to features. In this paper, we propose a framework to improve independent feature transformations such as PCA (Principal Component...
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