  Automatic speech recognition (ASR) converts the human voice into readily
understandable and categorized text or words. Although Bengali is one of the
most widely spoken languages in the world, there have been very few studies on
Bengali ASR, particularly on Bangladeshi-accented Bengali. In this study, audio
recordings of spoken digits (0-9) from university students were used to create
a Bengali speech digits dataset that may be employed to train artificial neural
networks for voice-based digital input systems. This paper also compares the
Bengali digit recognition accuracy of several Convolutional Neural Networks
(CNNs) using spectrograms and shows that a test accuracy of 98.23% is
achievable using parameter-efficient models such as SqueezeNet on our dataset.
