Speech large language models (Speech LLMs) are increasingly expected to reason in everyday spoken interactions, where users often revise task conditions through self-correction. Unlike ordinary disfluencies, self-corrections alter the premise structure of a problem: obsolete conditions must be discarded, while final corrected conditions must guide reasoning. We formulate this setting as self-corrected speech reasoning and study it as a condition-update problem. We construct SCUR-Bench (Spoken Condition-Update Reasoning Benchmark), a controlled Chinese benchmark of 13,295 instances organized by reasoning difficulty and self-correction difficulty, and introduce a three-path diagnostic protocol to analyze speech-side recovery, condition use, and reasoning. Our analysis reveals a recurrent failure pattern in current Speech LLMs: corrected conditions may be perceived, transcribed, or even mentioned, yet still fail to serve as active premises in reasoning. We term this final-condition adoption failure. A controlled English parallel evaluation exhibits the same recovery–adoption gap, providing preliminary evidence that this failure is not unique to Chinese. To mitigate it, we propose PACER, a perception-aware condition-enhanced reinforcement framework based on GRPO. On Qwen2.5-Omni-7B, PACER reduces end-to-end error and condition-related errors by 39.8% and 67.5%, respectively, relative to the untrained baseline.

Neural transducers offer an alignment-free framework for speech-to-text modeling, and hierarchical transducer architectures further improve multilingual joint automatic speech recognition (ASR) and speech translation (ST) by stacking a translation-focused encoder on top of an ASR encoder. However, extending hierarchical transducers to multilingual many-to-many settings remains challenging: fully shared models often suffer from negative transfer and unstable target-language generation, while training separate models for each direction is computationally prohibitive. We propose LCMA-SRT (Language-Conditional Mixture-of-Experts Adapters for Speech Recognition and Translation), which augments a hierarchical transducer with language-conditional Mixture-of-Experts (MoE) adapters. A source-conditioned MoE adapter (SRC-MoE) uses source-language embeddings to reduce cross-language interference and improve multilingual ASR. A target-conditioned MoE adapter (TGT-MoE) uses the desired target language to reduce cross-target interference and stabilize target-language generation in many-to-many ST. Experiments on Europarl-ST (9 languages, 72 directions) show that LCMA-SRT improves both ASR and ST within a single joint model, reducing average WER and improving BLEU and COMET over strong hierarchical transducer baselines. We release our code and models at https://github.com/linanjie0820/LCMA-SRT.