Abstract
Native Language Identification is a prominent paralinguistic study with applications ranging from biometric analysis to speaker adaptation. Former studies on this task have benefited from alternative acoustic feature representations and pre-trained neural networks. In this work, we explore the Native Language Identification performance of contextual acoustic (wav2vec 2.0) and linguistic
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