Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem GMM-HMM System
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Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert speech into text. Alternately, the end goal is achieved by a combination of sub-tasks, namely, feature extraction, acoustic modeling and sequence decoding, ...
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 ...
This paper describes a high performance innovative and sustainable Speaker Identification (SID) solution, running over large voice samples database. The solution is based on development, integration and fusion of a series of speech analytic algorithms whic ...
This paper describes SIIP (Speaker Identification Integrated Project) a high performance innovative and sustainable Speaker Identification (SID) solution, running over large voice samples database. The solution is based on development, integration and fusi ...
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 ...
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 ...
In this paper, a compressive sensing (CS) perspective to exemplar-based speech processing is proposed. Relying on an analytical relationship between CS formulation and statistical speech recognition (Hidden Markov Models HMM), the automatic speech recognit ...
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 ...
Statistical speech recognition has been cast as a natural realization of the compressive sensing problem in this work. The compressed acoustic observations are sub-word posterior probabilities obtained from a deep neural network. Dictionary learning and sp ...