Mostakim, MoinRashid, WaridaReza, Mohi2018-01-152018-01-1520172017ID 14301026ID 14101040http://hdl.handle.net/10361/9059Cataloged from PDF version of thesis report.Includes bibliographical references (pages 31-33).This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.We have created an isolated-word dataset - Prodorshok 1, which consists of 34 Bengali words related to navigation with 1011 voice samples. The word set is intended to help design speaker dependent/independent, voice-command driven automated speech recognition (ASR) systems that can potentially improve human-computer interaction. This paper presents the results of an objective analysis that was undertaken using a subset of words from Prodorshok I to help 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, even when working with small datasets. Prodorshok I will be expanded upon and made publicly available for others to use under an Open Data License (ODbL).34 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Data augmentationSpeech recognitionThesis