A new arXiv paper introduces SURE-Voice, a training-free front-end component that estimates whether audio actually contains usable speech evidence before a speech language model generates output. The work frames the problem as pre-generation support estimation, targeting cases where speech LLMs produce plausible but unsupported responses from audio lacking meaningful speech.
A new arXiv paper introduces RetroThinker, a method that lets speech large language models revisit and revise earlier reasoning steps. Speech LLMs cut latency and preserve vocal cues lost in pipelines that combine speech recognition with text models, but they still trail text-only systems in capability. The work aims to narrow that gap by adapting retrospective reasoning to the speech setting.
A new arXiv paper proposes LOGIC, a method that injects contextual biasing into Speech LLMs within logit space rather than through prompting. The approach targets the difficulty these models have in recognizing newly emerging entities such as names, trends, and personalized terms. The authors report gains in both efficiency and robustness compared with prompt-based baselines.