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

• URL: http://arxiv.org/abs/2606.18597v1
• Date: Wed, 17 Jun 2026 01:46:41 GMT
• Title: Low-resource Language Discrimination Towards Chinese Dialects with Transfer learning and Data Augmentation
• Abstract summary: We develop a novel Chinese dialects discrimination framework with transfer learning and data augmentation.<n>Our model significantly outperforms state-of-the-art methods on two benchmark Chinese dialects corpora.
• Score: 12.00948846037621
• License: http://creativecommons.org/licenses/by/4.0/
• 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 augment the target-side low-resource Chinese dialects, and fine-tune another target ASR model based on the previous source-side ASR model. Meanwhile, the potential common semantic features between source-side and target-side ASR models can be captured by using self-attention mechanism. Finally, we extract the hidden semantic representation in the target ASR model to conduct Chinese dialects discrimination. Our extensive experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two benchmark Chinese dialects corpora.

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