Multimodal self-consistency with audio-language models reaches 52.56% accuracy on utterance-level MI coding from five audio sessions, beating single-pass baselines.
Seeing and Hearing What Has Not Been Said: A multimodal client behavior classifier in Motivational Interviewing with interpretable fusion
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Leveraging Multimodal Self-Consistency Reasoning in Coding Motivational Interviewing for Alcohol Use Reduction
Multimodal self-consistency with audio-language models reaches 52.56% accuracy on utterance-level MI coding from five audio sessions, beating single-pass baselines.