We describe the winning system for Task 2 of the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization. The task requires producing fully diacritized Arabic text from speech audio and undiacritized transcripts, with only 2,327 training samples available and no external data permitted. Our system fine-tunes CATT-Whisper, a character-level multimodal model combining a pretrained CATT text encoder with a frozen Whisper speech encoder. The key to our approach is training regularization: R-Drop consistency regularization, Optuna-optimized hyperparameters with high weight decay, and Focal Loss. At inference, we average 200 stochastic forward passes across four model checkpoints using Monte Carlo Dropout at the softmax probability level. The system achieves 23.26% WER on the primary leaderboard metric (with case endings, including no-diacritic positions), placing 1st among all participants.

翻译：本文描述了KSAA-2026阿拉伯语音符标注自动听写共享任务Task 2的优胜系统。该任务要求从语音音频和无音符转录文本中生成完全标注音符的阿拉伯语文本，仅提供2,327个训练样本且禁止使用外部数据。我们的系统对CATT-Whisper进行微调——该模型是一种结合预训练CATT文本编码器与冻结Whisper语音编码器的字符级多模态模型。方法核心在于训练正则化技术：R-Drop一致性正则化、基于Optuna优化的高权重衰减超参数，以及焦点损失函数。推理阶段，我们通过蒙特卡洛Dropout在softmax概率层面对四个模型检查点的200次随机前向传播结果进行平均。系统在主排行榜指标（含词尾变化，包括无音符位置）上实现23.26%的词错误率，在所有参与者中位列第一。