Optuna
optuna-3bb638b0·2 events·first seen 22d agoAliases: Optuna
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Thaka Wins KSAA-2026 Arabic Speech Diacritization Task with Regularized Fine-Tuning of CATT-Whisper
The Thaka team describes their winning system for Task 2 of the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization, which requires producing fully diacritized Arabic text from speech audio and undiacritized transcripts. Their approach fine-tunes CATT-Whisper, a multimodal model combining a CATT text encoder with a frozen Whisper speech encoder, under severe data constraints (2,327 training samples, no external data). Key techniques include R-Drop consistency regularization, Optuna-optimized hyperparameters with high weight decay, Focal Loss, and Monte Carlo Dropout inference averaging over 200 stochastic forward passes across four checkpoints. The system achieves 23.26% WER on the primary metric, placing first among all participants.
Systematic framework for selecting trajectories in data augmentation evaluated across five strategies
A thesis-derived arXiv preprint proposes a framework for evaluating five trajectory selection strategies—Outlierness, Diversity, Representativeness, Uncertainty, and Random—for data augmentation in spatio-temporal ML tasks. The study tests these strategies across four datasets spanning animal behavior, maritime, and urban traffic domains using linear and non-linear models with Optuna-based hyperparameter optimization. Key findings show systematic strategies (especially Outlierness and Uncertainty) outperform random selection in sparse datasets but can degrade performance in dense, high-quality datasets, with UMAP visualization confirming topological effects.