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This paper presents the SEAR system for multilingual speech MCQ, achieving 90.92% accuracy on the MLC-SLM Challenge through segment-evidence-aware routing, supervised fine-tuning, and reinforcement learning.
This paper presents Aslema, a system for the NADI 2026 shared task on spoken language understanding, using fine-tuned audio LLMs and synthetic data augmentation to improve intent recognition and slot filling for Tunisian Derja.
Introduces SFL-MTSC, a structured aggregation framework for robust multi-intent spoken language understanding using LLM self-consistency at the semantic frame level, showing improved slot F1 and overall accuracy on the MAC-SLU benchmark.
Proposes BindingSubspace (BSU), a representation-level framework that isolates and attenuates intent-conditioned directions in end-to-end spoken language understanding models to prevent capability persistence, where suppressing an intent still allows slot generation under forced prefixes. The method reduces forced-prefix recoverability while preserving retained performance on SLU benchmarks.