  Bengali is a prominent language of the Indian subcontinent. However, while
many state-of-the-art acoustic models exist for prominent languages spoken in
the region, research and resources for Bengali are few and far between. In this
work, we apply CTC based CNN-RNN networks, a prominent deep learning based
end-to-end automatic speech recognition technique, to the Bengali ASR task. We
also propose and evaluate the applicability and efficacy of small 7x3 and 3x3
convolution kernels which are prominently used in the computer vision domain
primarily because of their FLOPs and parameter efficient nature. We propose two
CNN blocks, 2-layer Block A and 4-layer Block B, with the first layer
comprising of 7x3 kernel and the subsequent layers comprising solely of 3x3
kernels. Using the publicly available Large Bengali ASR Training data set, we
benchmark and evaluate the performance of seven deep neural network
configurations of varying complexities and depth on the Bengali ASR task. Our
best model, with Block B, has a WER of 13.67, having an absolute reduction of
1.39% over comparable model with larger convolution kernels of size 41x11 and
21x11.
