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We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low- dimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse represen ...
Automatic visual speech recognition is an interesting problem in pattern recognition especially when audio data is noisy or not readily available. It is also a very challenging task mainly because of the lower amount of information in the visual articulati ...
We cast the query by example spoken term detection (QbE-STD) problem as subspace detection where query and background subspaces are modeled as union of low-dimensional subspaces. The speech exemplars used for subspace modeling are class-conditional posteri ...
For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...
Multi-session training conditions are becoming increasingly common in recent benchmark datasets for both text-independent and text-dependent speaker verification. In the state-of-the-art i-vector framework for speaker verification, such conditions are addr ...
Automatic speech recognition (ASR) is a fascinating area of research towards realizing humanmachine interactions. After more than 30 years of exploitation of Gaussian Mixture Models (GMMs), state-of-the-art systems currently rely on Deep Neural Network (DN ...
We cast the problem of query by example spoken term detection (QbE-STD) as subspace detection where query and background are modeled as a union of low-dimensional subspaces. The speech exemplars used for subspace modeling consist of class-conditional poste ...
We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of lowdimensional subspaces. To that end, the training posteriors are used for dictionary learning and sparse coding. Sparse representa ...
Acoustic modeling based on deep architectures has recently gained remarkable success, with substantial improvement of speech recognition accuracy in several automatic speech recognition (ASR) tasks. For distant speech recognition, the multi-channel deep ne ...
Acoustic modeling based on deep architectures has recently gained remarkable success, with substantial improvement of speech recognition accuracy in several automatic speech recognition (ASR) tasks. For distant speech recognition, the multi-channel deep ne ...