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Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models
Conventional deep neural networks (DNN) for speech acoustic modeling rely on Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary class labels as the targets for DNN training.
Exploiting Low-dimensional Structures to Enhance DNN Based Acoustic Modeling in Speech Recognition
We propose to model the acoustic space of deep neural network (DNN) class-conditional posterior probabilities as a union of low-dimensional subspaces.
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees
This paper addresses the problem of ad hoc microphone array calibration where only partial information about the distances between microphones is available.
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