Clinical dialogue systems are increasingly vital for patient education and follow-up care; however, effective responses often depend on the clinical context that patients often fail to provide in detail. Responding directly to vague messages can therefore lead to generic or clinically misaligned advice, a challenge that is particularly pronounced in post-op oral cancer (OC) care due to speech impairment and functional limitations. Moreover, post-op OC patients often experience psychological distress, making safety-aware language more likely to arise in dialogue. Dialogue systems in this setting must therefore address both clarifying missing clinical context and ensuring psychological safety. We propose a safety-aware dialogue system that applies information-gain guided clarification before RAG-based response generation and screens user utterances for emotional distress and suicidal ideation. Expert evaluations show that the proposed system improves the quality and clinical appropriateness of generated responses relative to strong baselines, while the safety module closely aligns with expert judgments on clinically concerning utterances. Furthermore, we release a clinically curated Chinese post-op OC QA dataset with expert-validated annotations, which we use throughout our experiments.

This paper presents a comprehensive approach for the Formosa Speech Recognition Challenge 2025 (FSR-2025), targeting automatic speech recognition (ASR) for the under-resourced Dapu and Zhao’an dialects of Taiwanese Hakka. Our method integrates data augmentation and robustness techniques, including SpecAugment, dialect-aware special tokens, text-to-speech (TTS) augmentation, noise/reverberation mixing, and speed perturbation, to mitigate data scarcity and domain mismatch. Experiments on the official FSR-2025 datasets show consistent improvements in both character error rate (CER) and word error rate (WER). Extensive ablation studies further confirm that each component contributes positively. These results offer a practical path toward robust ASR for under-resourced Hakka dialects and suggest broader applicability to other low-resource languages.