This study presents our system for Hakka Speech Recognition Challenge 2025. We designed and compared different systems for two low-resource dialects: Dapu and Zhaoan. On the Pinyin track, we gain boosts by leveraging cross-lingual transfer-learning from related languages and combining with self-supervised learning (SSL). For the Hanzi track, we employ pretrained Whisper with Low-Rank Adaptation (LoRA) fine-tuning. To alleviate the low-resource issue, two data augmentation methods are experimented with: simulating conversational speech to handle multi-speaker scenarios, and generating additional corpus via text-to-speech (TTS). Results from the pilot test showed that transfer learning significantly improved performance in the Pinyin track, achieving an average character error rate (CER) of 19.57%, ranking third among all teams. While in the Hanzi track, the Whisper + LoRA system achieved an average CER of 6.84%, earning first place among all. This study demonstrates that transfer learning and data augmentation can effectively improve recognition performance for low-resource languages. However, the domain mismatch seen in the media test set remains a challenge. We plan to explore in-context learning (ICL) and hotword modeling in the future to better address this issue.

End-to-End Neural Diarization (EEND) has undergone substantial development, particularly with powerset classification methods that enhance performance but can exacerbate speaker confusion. To address this, we propose a novel training strategy that complements the standard cross entropy loss with an auxiliary ordinal log loss, guided by a distance matrix of speaker combinations. Our experiments reveal that while this approach yields significant relative improvements of 15.8% in false alarm rate and 10.0% in confusion error rate, it also uncovers a critical trade-off with an increased missed error rate. The primary contribution of this work is the identification and analysis of this trade-off, which stems from the model adopting a more conservative prediction strategy. This insight is crucial for designing more balanced and effective loss functions in speaker diarization.