AUTOMATIC MISPRONUNCIATION DETECTION FOR GE’EZ  LANGUAGE USING DEEP LEARNING APPROACH

ABRAHAM, DEMLEW

dc.contributor.author	ABRAHAM, DEMLEW
dc.date.accessioned	2023-06-19T07:32:20Z
dc.date.available	2023-06-19T07:32:20Z
dc.date.issued	2022-09
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/15399
dc.description.abstract	Ge‘ez is an ancient Semitic language of Ethiopia, which survives as the liturgical language of the Ethiopian Orthodox Tewahedo Church (EOTC) and Eritrean Orthodox Tewahedo Church. Beyond EOTC, several universities and colleges in Ethiopia and abroad are offering courses in Ge‘ez language and literature. Ge‘ez has kept not only EOTC liturgical manuscripts but also Ethiopian history, culture and huge wisdom about medicine, astronomy, mathematics, philosophy and several others. Ge‘ez language has unique pronunciation styles; knowing them is a key requirement. Pronunciation of Ge‘ez word has significant implications for the meaning of the word. It includes two broad pronunciation style categories, namely major and minor. Those categories include several others, which can be characterized by different features and become distinguishable with their unique characteristics. Studying Ge‘ez pronunciation styles is a key part of the language and it is a challenging task especially for beginners. The proposed system aimed to detect mispronunciation of Ge‘ez words speech automatically. It includes data acquisition, preprocessing, feature extraction, and classification. In audio signal processing, we recorded both correctly pronounced and mispronounced words from EOTC scholars and transformed the raw audio into MFCC and Mel spectrogram format. We proposed two classifications: classification for mispronunciation detection and classification for major pronunciation styles. Two models were built and tested using different feature extraction techniques. The experimental result of end-to-end CNN model for mispronunciation detection classification achieve 91% accuracy using MFCC and 97% using Mel spectrogram features. And for major Ge‘ez pronunciation styles classification, 94% accuracy using MFCC and 97% accuracy using Mel spectrogram were achieved. For mispronunciation detection, hybrid model or CNN with SVM achieve an accuracy of 88.4% and 95% using MFCC and Mel spectrogram, respectively. In terms of major Ge'ez pronunciation styles classification, the MFCC achieve 92% accuracy and the Mel spectrogram achieve 94.5%. Key word: Ge‘ez, Ge‘ez pronunciation styles, CNN, MFCC, Mel spectrogram	en_US
dc.language.iso	en_US	en_US
dc.subject	Computing	en_US
dc.title	AUTOMATIC MISPRONUNCIATION DETECTION FOR GE’EZ LANGUAGE USING DEEP LEARNING APPROACH	en_US
dc.type	Thesis	en_US