Reza, MohiRashid, WaridaMostakim, Moin2026-08-132026-08-132018-02-09M. Reza, W. Rashid and M. Mostakim, "Prodorshok I: A bengali isolated speech dataset for voice-based assistive technologies: A comparative analysis of the effects of data augmentation on HMM-GMM and DNN classifiers," 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, Bangladesh, 2017, pp. 396-399, doi: 10.1109/R10-HTC.2017.8288983.97815386217522-s2.0-85047405963https://hdl.handle.net/10361/29046Prodorshok 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.396-399en-USfalseAssistive technologyAutomatic speech recognitionBengaliDeep neural networkGaussian mixture modelHidden markov ModelHuman computer interactionAutomatic speech recognition.Bengali language.Human-computer interaction.Conference Proceeding10.1109/R10-HTC.2017.8288983