2017
Very deep convolutional neural networks for raw waveforms
ICASSP 2017accepted
Learning acoustic models directly from the raw waveform data with minimal processing is challenging. Current waveform-based models have generally used very few (~2) convolutional layers, which might be insufficient for building high-level discriminative features. In this work, we propose very deep c…