Dialect and Accent Identification for Afan Oromo Language using Hidden Markov Model Approach

Abstract

A fundamental challenge for current research on speech science and technology is under standing and modeling individual variation in spoken language. Individuals have their own speaking styles, depending on many factors, such as their dialect and accent as well as their socioeconomic background. These individual differences typically introduce modeling difficulties for large-scale speaker-independent systems designed to process input from any variant of a given language. This research focuses on automatically identifying the dialect or accent of Afan Oromo language speakers given a sample of their speech, and demonstrates how such a technology can be employed to improve Afan Oromo Speech Recognition. In this thesis, we describe a variety of approaches which are used to develop Automatic speech recognitions and dialect identifications. In particular, we examine stochastic (statistical) approach or Hidden Markov Model (HMM) which is used to develop Afan Oromo Dialect and Accent Identification in this study. In this study, a self-prepared corpus is used and the developed dialect and accent identifier is built on medium data size collected from specified target dialect area. The language model used is a bigram language model. Finally, the experimental result of this study is evaluated using word error rates (WER) and the performance achieved for the developed model is 41.45% Word Error Rate.