Speaker Recognition by Combining Features for Myanmar Weather Forecast Domain

Nowadays, speaker recognition has become one of the important application area of digital signal processing. Speech corpus is important in developing the speech processing and the development of the corpus is essential for low-resourced languages. Burmese (Myanmar language) can be recognized as a low-resourced language because of lack of available resources for speech processing research. Speaker Recognition (SReg) is an approach used to automatically recognize a speaker from their speech utterance. The main concept of SReg is to recognize the information of the speaker’s identity. In SReg, various features have been extracted to reflect the characteristics of the speakers. In this paper, an effective multi-feature combination and comparison of performance between the different size of data are proposed. In this work, weather news from Department of Meteorology and Hydrology, Myanmar is collected. The total size of the implemented Burmese speech corpus is over 10 hours and it contained 13 females and 3 males. The dataset is split into training data and testing data in 4:1 ratio. The experimental results on 16 speakers show that the proposed Burmese speaker recognition based on multi-feature combination achieved 99.16% accuracy and high applicability.