This paper describes the Resonate team’s submission to the NADI 2026 shared task on multidialectal Arabic speech processing. We participated in the robust dialectal Arabic ASR (Subtask 1.1) and spoken Arabic dialect identification (Task 2) subtasks, adding no external labelled speech. Our best single ASR model is a fully fine-tuned Cohere Transcribe Arabic, a 2.07B-parameter Arabic speech model, trained on 239.6K rows derived entirely from the 12.8K official clips through voice conversion, in-corpus noise augmentation and short-clip re-segmentation. Our submitted entry combines nine checkpoints of four sibling runs with a pivot-anchored ROVER vote and a per-dialect orthographic canonicaliser, obtaining 50.29% WER and 24.43% CER on the official test set and ranking second of nine teams. Our dialect identification system is a fine-tuned WavLM Base+ encoder with context-aware multi-head factorized attentive pooling under an angular-margin objective, which obtained 94% accuracy on the validation set and 41% on the out-of-domain test set. The ASR comparisons support augmenting scarce dialectal transcripts with acoustic variation, while ADI highlights the need for evaluation across domains. Code and reproducibility resources for both tasks are available at https://github.com/ResonateResearch/resonate-nadi-2026.