JOURNAL ARTICLE

A Comparative Reconstruction of Sister Languages: Oruhaya, Oluganda and Kiswahili.

  • Published In: Journal of Linguistics & Language in Education, 2024, v. 18, n. 1. P. 123 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Fredinand, Alfredina 3 of 3

Abstract

The study investigates the comparative reconstruction of three sister languages, namely Oruhaya, Oluganda and Kiswahili. Specifically, it analyzes lexical verbs, nouns, adjectives and adverbs using the phonetic plausibility principle. The study was conducted in Missenyi District, Kagera Region, where the three languages are spoken. Data were obtained by interviewing three native speakers of Oruhaya and Oluganda, and five teachers who teach Kiswahili in public primary schools. Additional data was obtained from the Oruhaya dictionary, Oruhaya riddles, proverbs and songs, as well from the Luganda-English dictionary and a dictionary of Swahili proverbs and their usage. The study found that most Bantu language words have sound correspondence, since they share a parent, that is, proto-Bantu. The sounds of the three sister languages have the same status and undergo change. The recommendation and suggestions of this study are that similar studies of comparative reconstruction in other Bantu and non-Bantu languages should be conducted to obtain an overall description of the phenomenon under study. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Linguistics & Language in Education. 2024/01, Vol. 18, Issue 1, p123
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2024
  • ISSN:0856-9965
  • DOI:10.56279/jlle.v18i1.8
  • Accession Number:179290482
  • Copyright Statement:Copyright of Journal of Linguistics & Language in Education is the property of University of Dar es Salaam, Department of Foreign Languages & Linguistics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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