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Overlapped speech recognition from a jointly learned multi-channel neural speech extraction and representation
[article]
2019
arXiv
pre-print
We propose an end-to-end joint optimization framework of a multi-channel neural speech extraction and deep acoustic model without mel-filterbank (FBANK) extraction for overlapped speech recognition. First, based on a multi-channel convolutional TasNet with STFT kernel, we unify the multi-channel target speech enhancement front-end network and a convolutional, long short-term memory and fully connected deep neural network (CLDNN) based acoustic model (AM) with the FBANK extraction layer to build
arXiv:1910.13825v1
fatcat:6bjdns3zfzajficgqoeg3722rq