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Sparse Non-negative Matrix Factorization (SNMF) and Deep Neural Networks (DNN) have emerged individually as two efficient machine learning techniques for single-channel speech enhancement. Nevertheless, there are only few works investigating the combination of SNMF and DNN for speech enhancement and robust Automatic Speech Recognition (ASR). In this paper, we present a novel combination of speech...
This paper investigates deep neural networks (DNN) based on nonlinear feature mapping and statistical linear feature adaptation approaches for reducing reverberation in speech signals. In the nonlinear feature mapping approach, DNN is trained from parallel clean/distorted speech corpus to map reverberant and noisy speech coefficients (such as log magnitude spectrum) to the underlying clean speech...
In this paper we present our contribution to the third CHiME challenge on speech separation and recognition for noisy multi-channel recordings. The use-case of the challenge consists in single speaker utterances recorded in highly non-stationary noisy environments using a 6-microphone array mounted on a tablet computer. The front-end of our system is performing speech enhancement by cascading a cross-correlation-based...
One class of feature enhancement techniques improve features robustness by performing temporal filtering to smooth the feature trajectories. While smoothing can enhance the features robustness by reducing the intra-class variation of the features, it also compromises the features discriminative power by reducing their inter-class distance. In this paper, we investigate the effect of feature smoothing...
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