Deep causal speech enhancement and recognition using efficient long-short term memory Recurrent Neural Network
Zhenqing Li,
Abdul Basit,
Amil Daraz
et al.
Abstract:Long short-term memory (LSTM) has been effectively used to represent sequential data in recent years. However, LSTM still struggles with capturing the long-term temporal dependencies. In this paper, we propose an hourglass-shaped LSTM that is able to capture long-term temporal correlations by reducing the feature resolutions without data loss. We have used skip connections in non-adjacent layers to avoid gradient decay. In addition, an attention process is incorporated into skip connections to emphasize the es… Show more
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