![[2602.22522] Efficient Dialect-Aware Modeling and Conditioning for Low-Resource Taiwanese Hakka Speech Processing](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22522] Efficient Dialect-Aware Modeling and Conditioning for Low-Resource Taiwanese Hakka Speech Processing
Summary
This article presents a novel framework for improving automatic speech recognition (ASR) for the low-resource Taiwanese Hakka language by addressing dialectal variability through dialect-aware modeling.
Why It Matters
The study is significant as it tackles the challenges of processing an endangered language with high dialectal diversity. By enhancing ASR performance, it contributes to the preservation and accessibility of Taiwanese Hakka, potentially benefiting linguistic research and technology development for low-resource languages.
Key Takeaways
- Introduces a unified framework using Recurrent Neural Network Transducers (RNN-T) for Taiwanese Hakka ASR.
- Implements dialect-aware modeling to separate dialectal variations from linguistic content.
- Achieves significant error rate reductions in ASR tasks for both Hanzi and Pinyin.
- Demonstrates the first systematic investigation of Hakka dialectal impacts on ASR.
- Highlights the synergy between cross-script objectives to enhance model performance.
Computer Science > Computation and Language arXiv:2602.22522 (cs) [Submitted on 26 Feb 2026] Title:Efficient Dialect-Aware Modeling and Conditioning for Low-Resource Taiwanese Hakka Speech Processing Authors:An-Ci Peng, Kuan-Tang Huang, Tien-Hong Lo, Hung-Shin Lee, Hsin-Min Wang, Berlin Chen View a PDF of the paper titled Efficient Dialect-Aware Modeling and Conditioning for Low-Resource Taiwanese Hakka Speech Processing, by An-Ci Peng and 5 other authors View PDF HTML (experimental) Abstract:Taiwanese Hakka is a low-resource, endangered language that poses significant challenges for automatic speech recognition (ASR), including high dialectal variability and the presence of two distinct writing systems (Hanzi and Pinyin). Traditional ASR models often encounter difficulties in this context, as they tend to conflate essential linguistic content with dialect-specific variations across both phonological and lexical dimensions. To address these challenges, we propose a unified framework grounded in the Recurrent Neural Network Transducers (RNN-T). Central to our approach is the introduction of dialect-aware modeling strategies designed to disentangle dialectal "style" from linguistic "content", which enhances the model's capacity to learn robust and generalized representations. Additionally, the framework employs parameter-efficient prediction networks to concurrently model ASR (Hanzi and Pinyin). We demonstrate that these tasks create a powerful synergy, wherein the cross-scr...
