AUTOMATIC SPEECH RECOGNITION FOR RAKHINE LANGUAGE USING HIDDEN MARKOV MODEL AND GAUSSIAN MIXTURE MODEL

Abstract

The automatic recognition of speech means enabling a natural and easy mode of interaction between human and machine. The process of speech recognition is to translate speech signal into text sequence. Automatic Speech Recognition (ASR) has been carried out by many researchers for their particular languages to provide their nations in language technologies. Therefore, this thesis aims to develop automatic speech recognition for Rakhine language, one of the main ethnic groups in Myanmar. Rakhine language is a low-resourced language and speech data are no freely available. Thus, in this work, speech corpus is built on two domains: broadcast news and daily conversations data. Broadcast news is collected from the web and the conversational data is recorded by uttering with own voice. This corpus is applied to develop the Rakhine ASR. Feature extraction is one of the components of ASR and its function is to extract feature from incoming speech signals. In this work, Mel Frequency Cepstral Coefficient (MFCC) feature extraction technique is used. Because of the phonetic dictionary is essential part for implementing Rakhine speech recognition system, pronunciation lexicon is built for Rakhine language in this work. And, Rakhine language model is also created by utilizing n-gram. In developing ASR, acoustic models is the crucial component and is established the connection between acoustic feature and phonetic. For this Rakhine ASR research, the Gaussian Mixture based Hidden Markov Model (HMM-GMM) is utilized. By using HMM-GMM, Rakhine ASR performance gets promising results.