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

Researchers developed a Chinese dialects discrimination framework using transfer learning and data augmentation to address low-resource challenges. The model outperformed state-of-the-art methods on two benchmark corpora by training a source ASR model and fine-tuning a target model with augmented data.

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 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.