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Denoising autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoenco...
In this paper, we propose a multi-level, contextdependent acoustic modeling framework for automatic speech recognition. For each context-dependent unit considered by the recognizer, we construct a set...
In this paper, we investigate a closed-loop auditory model and explore its potential as a feature representation for speech recognition. The closed-loop representation consists of an auditory-based, e...
In this paper we propose a back-off discriminative acoustic model for Automatic Speech Recognition (ASR). We use a set of broad phonetic classes to divide the classification problem originating from c...
To advance research, it isimportant to identify prom-ising future research direc-tions, especially those thathave not been adequately pursued or funded in the past. The working group producing this ar...
Speech recognition in adverse environment is one of the major issue in automatic speech recognition nowadays. While most current speech recognition system show to be highly efficient for ideal envir...
Automatic speech recognition (ASR) performs well under restricted conditions, but performance degrades in noisy environments. Audio-Visual Speech Recognition(AVSR) combats this by incorporating a visu...

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