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.

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.