Isolated Swahili words recognition using Sphinx4

Abstract

Speech recognition is one of the frontiers in Human Computer Interaction. A number of tools used to achieve speech recognition are currently available. One of such tools is Sphinx4 from Carnegie Mellon University (CMU). It has a recognition engine based on discrete Hidden Markov Model (dHMM) and a modular structure making it flexible to a diverse set of requirements. However, most efforts that have been undertaken using this tool are focused on established dialects such as English and French. Despite Swahili being a major spoken language in Africa, literature search indicates that little research has been undertaken in developing a speech recognition tool for this dialect. In this paper, we propose an approach to building a Swahili speech recognizer using Sphinx4 to demonstrate its adaptability to recognition of spoken Swahili words. To realize this, we examined the Swahili language structure and sound synthesis processes. Then, a 40 word Swahili acoustic model was built based on the observed language and sound structures using CMU Sphinx train and associate tools. The developed acoustic model was then tested using sphinx4.