  Prodorshok I is a Bengali isolated word dataset tailored to help create
speaker-independent, voice-command driven automated speech recognition (ASR)
based assistive technologies to help improve human-computer interaction (HCI).
This paper presents the results of an objective analysis that was undertaken
using a subset of words from Prodorshok I to assess its reliability in ASR
systems that utilize Hidden Markov Models (HMM) with Gaussian emissions and
Deep Neural Networks (DNN). The results show that simple data augmentation
involving a small pitch shift can make surprisingly tangible improvements to
accuracy levels in speech recognition.
