most citedUnsupervised Training for Deep Speech Source Separation with Kullback-Leibler Divergence Based Probabilistic Loss Function

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cs.CL2022

InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR

Yu Nakagome, Tatsuya Komatsu, Yusuke Fujita +2

This paper proposes InterAug: a novel training method for CTC-based ASR using augmented intermediate representations for conditioning. The proposed method exploits the conditioning…

eess.AS2019★ 1 cited

Unsupervised Training for Deep Speech Source Separation with Kullback-Leibler Divergence Based Probabilistic Loss Function

Masahito Togami, Yoshiki Masuyama, Tatsuya Komatsu +1

In this paper, we propose a multi-channel speech source separation with a deep neural network (DNN) which is trained under the condition that no clean signal is available. As an al…