In recent years, large-scale pre-trained speech models such as Whisper have been widely applied to speech recognition. While they achieve strong performance on high-resource languages such as English and Mandarin, dialects and other low-resource languages remain challenging due to limited data availability. The government-led “Formosa Speech in the Wild (FSW) project” is an important cultural preservation initiative for Hakka, a regional dialect, where the development of Hakka ASR systems represents a key technological milestone. Beyond model architecture, data processing and training strategies are also critical. In this paper, we explore data augmentation techniques for Hakka speech, including TTS and MUSAN-based approaches, and analyze different data combinations by fine-tuning the pre-trained Whisper model. We participated in the 2025 Hakka FSR ASR competition (student track) for the Dapu and Zhaoan varieties. In the pilot test, our system achieved 7th place in Hanzi recognition (CER: 15.92) and 3rd place in Pinyin recognition (SER: 20.49). In the official finals, our system ranked 6 in Hanzi recognition (CER: 15.73) and 4 in Pinyin recognition (SER: 20.68). We believe that such data augmentation strategies can advance research on Hakka ASR and support the long-term preservation of Hakka culture.

This paper presents a framework to answer the questions that require various kinds of inference mechanisms (such as Extraction, Entailment-Judgement, and Summarization). Most of the previous approaches adopt a rigid framework which handles only one inference mechanism. Only a few of them adopt several answer generation modules for providing different mechanisms; however, they either lack an aggregation mechanism to merge the answers from various modules, or are too complicated to be implemented with neural networks. To alleviate the problems mentioned above, we propose a divide-and-conquer framework, which consists of a set of various answer generation modules, a dispatch module, and an aggregation module. The answer generation modules are designed to provide different inference mechanisms, the dispatch module is used to select a few appropriate answer generation modules to generate answer candidates, and the aggregation module is employed to select the final answer. We test our framework on the 2020 Formosa Grand Challenge Contest dataset. Experiments show that the proposed framework outperforms the state-of-the-art Roberta-large model by about 11.4%.