We describe Thakaa’s systems for NADI 2026 subtasks 1.1 and 1.3 and Task 2, covering noisy country-level Arabic automatic speech recognition (ASR), Tunisian code-switched ASR, and spoken dialect identification. The ASR systems combine heterogeneous connectionist temporal classification (CTC), encoder–decoder, and audio-LLM recognizers through ROVER, with country-aware routing for subtask 1.1 and a frame-rate-corrected CTC anchor for subtask 1.3. The dialect identifier fuses Ara-BEST-RQ acoustic scores with lexical evidence extracted from Cohere and Whisper transcripts by a partially fine-tuned MARBERTv2 model. Our official test scores were 50.41% WER on subtask 1.1, 14.53% WER on subtask 1.3, and 56.15% accuracy on Task 2, ranking third, third, and first, respectively. Across the three systems, the largest gains came from explicit control of alignment, temporal resolution, and evidence diversity rather than from model scale alone.