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

2026-06-18 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A novel framework, Chinese Dialects Discrimination with Transfer Learning and Data Augmentation (CDDTLDA), has been developed to address the challenge of scarce annotation resources in Chinese dialect discrimination. Submitted on June 17, 2026, and published in ACM TALLIP, this method first trains a source-side automatic speech recognition (ASR) model using a larger Chinese dialects corpus. It then applies data augmentation techniques, including speed, pitch, and noise disturbance, to low-resource target-side Chinese dialects. A target ASR model is subsequently fine-tuned from the pre-trained source model, incorporating a self-attention mechanism to capture common semantic features. Finally, hidden semantic representations from the target ASR model are extracted for dialect discrimination. Experimental results show CDDTLDA significantly outperforms existing methods on two benchmark Chinese dialects corpora.

Key takeaway

For NLP Engineers developing speech-based solutions for low-resource Chinese dialects, this framework offers a proven strategy. You should consider pre-training ASR models on larger related corpora and systematically applying acoustic data augmentation (speed, pitch, noise) to your limited target data. This approach, combined with transfer learning and self-attention, can significantly improve discrimination accuracy, enabling robust applications where data scarcity was previously a barrier.

Key insights

Transfer learning and data augmentation effectively overcome low-resource challenges in Chinese dialect discrimination.

Principles

• Pre-train ASR on larger source corpora.
• Augment low-resource data via speed, pitch, noise.
• Self-attention captures common semantic features.

Method

The CDDTLDA framework trains a source ASR model, augments target low-resource dialects with speed/pitch/noise, fine-tunes a target ASR model using the source model and self-attention, then extracts hidden representations for discrimination.

In practice

• Apply ASR pre-training to similar low-resource NLP tasks.
• Use speed, pitch, noise for speech data augmentation.
• Integrate self-attention for cross-domain feature learning.

Topics

• Chinese Dialects
• Language Discrimination
• Transfer Learning
• Data Augmentation
• Automatic Speech Recognition
• Low-Resource NLP

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.