We describe the KAND CA systems for all five subtasks of the NADI 2026 shared task on multidialectal Arabic speech processing. For the three ASR subtasks, a single family of staged, fully fine-tuned Whisper large-v3 models — broad dialectal adaptation, then only task-matched gold data — is combined with task-specific levers: an mT5 spellingcorrection post-editor trained on the task references (Subtask 1.1, final country-average WER 0.5111), chained continue-fine-tuning through the Subtask 1.1 data before the Mixed- ASR dev split (Subtask 1.2, WER 0.48), and a fresh two-epoch schedule over a 46K-clip external gold Tunisian–French corpus with an accent-only French post-pass (Subtask 1.3, WER 15.21%). For spoken dialect identification (Task 2), where in-domain accuracy anti-predicted blind-test accuracy, our system fuses an architecturally distinct FastConformerencoder classifier, a decorrelated enhancedaudio view, and an ASR-transcript text route, reaching 0.5114 accuracy. For dialectal TTS (Subtask 3) we submitted 4,001 voice-cloned clips across ten dialects (UTMOS 2.72). We also report extensive negative results and release our models, correctors, and code publicly.