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Multi-class identification of tonal contrasts in Chokri using supervised machine learning algorithms

Linguistics and Languages

Multi-class identification of tonal contrasts in Chokri using supervised machine learning algorithms

A. Gope, A. Pal, et al.

This groundbreaking study by Amalesh Gope, Anusuya Pal, Sekholu Tetseo, Tulika Gogoi, Joanna J, and Dinkur Borah explores the use of machine learning algorithms to identify tonal contrasts in the endangered Chokri language. With astonishing accuracy rates reaching 95-97% for male speakers, the research uncovers the potential of these algorithms in analyzing tonal languages, paving the way for broader applications.

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