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
The development of more sophisticated AI models and readily available computational resources enables research into previously resource-scarce linguistic challenges.
This research indicates progress in overcoming data scarcity for specific, culturally significant language tasks, which is crucial for broader AI adoption and localized applications.
The ability to accurately discriminate between Chinese dialects using limited data opens doors for improving communication, cultural preservation, and market access within China.
- · Chinese tech companies
- · Linguistics researchers
- · Populations speaking Chinese dialects
- · AI developers focused on low-resource languages
Improved accuracy for AI applications tailored to diverse Chinese-speaking populations.
Potential for enhanced digital content localization and more effective government services targeting specific dialect groups.
Increased social cohesion or, conversely, increased surveillance capabilities impacting privacy within distinct dialect communities.
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Read at arXiv cs.CL