3 citations · 16 across the 14 of their papers we have counts for
4 papers · 1 filter
Combining Spatial Clustering with LSTM Speech Models for Multichannel Speech Enhancement
Felix Grezes, Zhaoheng Ni, Viet Anh Trinh +1
Recurrent neural networks using the LSTM architecture can achieve significant single-channel noise reduction. It is not obvious, however, how to apply them to multi-channel inputs…
Improved MVDR Beamforming Using LSTM Speech Models to Clean Spatial Clustering Masks
Zhaoheng Ni, Felix Grezes, Viet Anh Trinh +1
Spatial clustering techniques can achieve significant multi-channel noise reduction across relatively arbitrary microphone configurations, but have difficulty incorporating a detai…
Enhancement of Spatial Clustering-Based Time-Frequency Masks using LSTM Neural Networks
Felix Grezes, Zhaoheng Ni, Viet Anh Trinh +1
Recent works have shown that Deep Recurrent Neural Networks using the LSTM architecture can achieve strong single-channel speech enhancement by estimating time-frequency masks. How…
Large scale evaluation of importance maps in automatic speech recognition
Viet Anh Trinh, Michael I Mandel
In this paper, we propose a metric that we call the structured saliency benchmark (SSBM) to evaluate importance maps computed for automatic speech recognizers on individual utteran…