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Models that leverage acoustic features can dramatically enhance the detection of nuanced emotional states in speech, outperforming traditional text-based approaches.
Current Full-Duplex Speech Language Models stumble in multi-round conversations, struggling to maintain consistent performance across turns and various evaluation dimensions.
