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Automatic Recognition of Kurdish Dialects and Neighbor Languages 
Abdulbasit Kamil Faeq
Department of Software Engineering, Faculty of Engineering, Koya University

Abstract: Dialect recognition is one of the hottest topics in the speech analysis area. In this study, a system for dialect and language recognition is developed using phonetic and a style-based features. The study suggests a new set of feature using one-dimensional local binary pattern (LBP). The results show that the proposed LBP set of the feature is useful to improve dialect and language recognition accuracy. The acquired data involved in this study are three Kurdish dialects (Sorani, Badini, and Hawrami) with three neighbor languages (Arabic, Persian, and Turkish). The study proposed a new method to interpret the closeness of the Kurdish dialects and their neighbor languages using confusion matrix and a non-metric multi-dimensional visualization technique. The result shows that the Kurdish dialects can be clustered and linearly separated from the neighbor languages

Keywords: Dialect recognition, Language processing, Speech analysis, Machine learning, Local binary pattern.

Date: 06/11/2017
Place: Faculty of Engineering/ Nashmeil Hall
Caroline Yousif Daniel,
Jan 12, 2018, 11:27 AM