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Deep Neural Network (DNN) have been extensively used in Automatic Speech Recognition (ASR) applications. Very recently, DNNs have also found application in detecting natural vs. spoofed speech at ASV spoof challenge held at INTERSPEECH 2015. Along the similar lines, in this work, we propose a new feature extraction architecture of DNN called the subband(More)
In this paper we describe the first use of laser desorption in conjunction with membrane introduction mass spectrometry (MIMS). In this technique, a low-powered carbon dioxide laser is used to irradiate the low-pressure (vacuum) side of a silicone membrane during a typical MIMS analysis of an aqueous solution. The absorption of laser energy results in(More)
In this paper, we propose a novel feature extraction architecture of Deep Neural Network (DNN), namely, subband autoencoder (SBAE). The proposed architecture is inspired by the Human Auditory System (HAS) and extracts features from speech spectrum in an unsupervised manner. We have used features extracted by this architecture for non-intrusive objective(More)
To emulate the human perception in quality assessment, an objective metric or assessment method is required, which is a challenging task. Moreover, assessing the quality of speech without any reference or the ground truth is altogether more difficult. In this paper, we propose a new non-intrusive speech quality assessment metric for objective evaluation of(More)
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