This paper describes TalTech's submissions to the Beyond Transcription Challenge (BeTraC), which requires generating SOAP notes directly from long doctor-patient conversation recordings, without intermediate transcription. After screening open-weight speech LLMs for long-audio robustness, we adapted Voxtral Mini (lightweight track) and Voxtral Small (heavyweight track) with LoRA supervised fine-tuning followed by DAPO reinforcement learning that uses the challenge metric, Open Medical Concept F1, as its reward. Our systems ranked first in both tracks, and an independent LLM-as-a-judge evaluation showed the lowest hallucination rate among all submissions, indicating that reinforcement learning against a concept-matching metric need not compromise factual reliability. We also find that fine-tuning on text transcripts transfers well to speech input and appears to improve robustness on out-of-domain real recordings.
Overall, matched-text speech delivery should be treated as a first-class factor in Audio LLM safety evaluation by holding transcript content fixed and varying six speech-delivery presets whose acoustic attributes may co-vary.
This work introduces CoSy, a novel framework for generating diverse, steerable, multi-turn conversations at scale and evaluates CoSy on conversational grounded reasoning tasks (i.e., answering questions based on contextual information), a core on-device use case.
Patrick Huber, Arash Einolghozati, Rylan Conway et al.· IEEE Games Entertainment Med...· 0 citations
Results show that supervised fine-tuning provides the largest gain, while synthetic-speech LoRA adaptation and reinforcement learning further improve robustness, while synthetic-speech LoRA adaptation and reinforcement learning further improve robustness.
Hao Wu, Rong-Qi Han, Zhen Wang et al.· 0 citations
DCASE~2026 Task~5 introduces Audio-Dependent Question Answering (ADQA), which tests whether large audio-language models answer from the audio rather than from textual priors. An Audio-Dependency Filtering (ADF) pipeline combines silent-audio probing, per-option perplexity, a large language model (LLM) commonsense check...
Haolin He, Renhe Sun, Zheqi Dai et al.· 1 citation
An automated pipeline for transcription and summarization of video conferences held on the Jitsi Meet platform that requires no proprietary cloud API keys and is deployable on-premise via Docker Compose, making it suitable for organizations with strict data-privacy requirements.
G. Amirkhanova, L. Bektemir, Shyrailym Adilkyzy et al.· International Conference on...· 0 citations
This work compares the capability of generative LLMs under a five-condition prompt ablation against a fine-tuned DistilBERT token classifier at detecting self-repairs and suggests that a locally deployable encoder, given sufficient in-domain annotation, is a more plausible route to clinical self-repair detection than s...
R. Wu, S. Pugh, K. O'Connor et al.· medRxiv· 0 citations
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