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Current speech-to-speech models may sound good, but they miss the mark on natural conversational dynamics, revealing critical areas for improvement.
Emotional entrainment detection can reach 97.01% accuracy by considering the temporal dynamics of dyadic speech interactions.
Speech-aware LLMs are surprisingly bad at speaker verification, but a simple embedding injection trick closes the gap with dedicated systems while preserving the LLM's language abilities.
LLMs can be tricked into making incorrect causal judgments by semantically similar questions, even with Chain-of-Thought prompting, highlighting a critical gap in true causal reasoning.
