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This work evaluates state-of-the-art LLMs as pointwise and pairwise judges of conversational success on CANDOR, finding pointwise scoring correlates moderately with human ratings, while pairwise comparison suffers from long transcripts and positional bias.
A lightweight backchannel head is introduced that predicts, from a full-duplex model's own hidden states, when a backchannel should begin, and once this probability crosses a tunable threshold, a backchannel is force-decoded.
A technique is proposed to automatically construct benchmarks of such attacks and apply it to measure leakage rates across two customisation mechanisms, prompting and fine-tuning, finding that fine-tuning without context prompts offers the best balance.
It is argued that progress requires dedicated speech-specific training data and models that genuinely condition on speech, and both text- and speech-based quality estimation metrics on two contrastive datasets targeting gender agreement and prosody fall short.
KIT's main contribution is a scalable data augmentation pipeline that unifies heterogeneous medical dialogue datasets through synthetic speech generation and automatically generated SOAP supervision, enabling robust adaptation of a speech foundation model for end-to-end speech-to-SOAP generation.
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