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This paper addresses the problem of robust speech recognition in noisy conditions in the framework of hidden Markov models (HMMs) and missing feature imputation techniques. It presents a new statistical approach to the detection and estimation of unreliable features based on a probabilistic measure and Gaussian mixture model (GMM) representing clean speech distribution. In the estimation process,...
Reconstruction of missing features promotes robustness in speaker recognition applications under noisy conditions. In this paper, we aim at enhancing the reliability of speech features for noise robust speaker identification under short training and testing sessions restrictions. Towards this direction, we apply a low-rank matrix recovery approach to reconstruct the unreliable spectrographic data...
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