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

2026-06-17 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A novel framework, CDDTLDA (Chinese Dialects Discrimination with Transfer Learning and Data Augmentation), addresses the challenge of low-resource Chinese dialect discrimination. This natural language processing task suffers from scarce annotation resources. The CDDTLDA framework first trains a source-side automatic speech recognition (ASR) model using a larger Chinese dialects corpus. It then augments target-side low-resource dialects with speed, pitch, and noise disturbance, followed by fine-tuning a target ASR model based on the pre-trained source model. A self-attention mechanism captures common semantic features between the ASR models. Finally, hidden semantic representations from the target ASR model are extracted for dialect discrimination. Experimental results demonstrate that CDDTLDA significantly outperforms established methods on two benchmark Chinese dialects corpora.

Key takeaway

For NLP engineers developing speech-based applications in low-resource languages, you should consider adopting a transfer learning and data augmentation strategy. This approach, exemplified by CDDTLDA, significantly improves performance on tasks like Chinese dialect discrimination by utilizing pre-trained ASR models and synthetic data generation. Evaluate integrating acoustic disturbances such as speed, pitch, and noise into your data augmentation pipelines to enhance model robustness and overcome annotation scarcity.

Key insights

Transfer learning and data augmentation effectively overcome scarce annotation resources for low-resource Chinese dialect discrimination.

Principles

• Pre-train ASR on larger source corpora.
• Augment low-resource data with acoustic disturbances.
• Fine-tune pre-trained models for target tasks.

Method

The CDDTLDA framework trains a source ASR model, augments target dialects via speed, pitch, and noise disturbance, fine-tunes a target ASR model, and uses self-attention to extract hidden semantic representations for discrimination.

In practice

• Apply speed, pitch, and noise for speech data augmentation.
• Use pre-trained ASR models for dialect-specific fine-tuning.
• Implement self-attention to align source and target features.

Topics

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

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.