Low-resource Language Discrimination Towards Chinese Dialects with Transfer learning and Data Augmentation

arXiv:2606.18597v1 Announce Type: new Abstract: Chinese dialects discrimination is a challenging natural language processing task due to scarce annotation resource. In this article, we develop a novel Chinese dialects discrimination framework with transfer learning and data augmentation (CDDTLDA) in order to overcome the shortage of resources. To be more specific, we first use a relatively larger Chinese dialects corpus to train a source-side automatic speech recognition (ASR) model. Then, we adopt a simple but effective data augmentation method (i.e., speed, pitch, and noise disturbance) to a

Source: arXiv cs.CL — read the full report at the original publisher.

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