3 papers - avg viability 6.0
A family of self-supervised learning models for multi-dialectal Arabic speech processing that achieves state-of-the-art performance on dialect identification.
This research introduces a novel training framework to significantly improve the robustness of speech-to-LLM models against noisy and error-prone contextual information during inference, leading to more reliable real-world performance.
A multilingual dataset for evaluating speech large language models with human-recorded prompts.