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Found 2 papers, 1 papers with code
Morphological Segmentation for Seneca
This study takes up the task of low-resource morphological segmentation for Seneca, a critically endangered and morphologically complex Native American language primarily spoken in what is now New York State and Ontario.
Fully Convolutional ASR for Less-Resourced Endangered Languages
The application of deep learning to automatic speech recognition (ASR) has yielded dramatic accuracy increases for languages with abundant training data, but languages with limited training resources have yet to see accuracy improvements on this scale.
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