arXiv:2606.31055v1 Announce Type: new Abstract: Speech-to-speech (S2S) AI agents are advancing rapidly, yet evaluation lacks interpretable speech-native measures for conversational prosody and rhythm. Because $F_0$, speaking rate, articulation rate, and pausing shift with model-predicted speaker traits and interaction state, pooled human statistics can be poorly calibrated for evaluating a particular output. Using 4000+ hours of dyadic English conversation from the Seamless Interaction dataset, we construct matched reference regimes for $F_0$ mean, $F_0$ expressivity, speech rate, articulation
The rapid advancement of S2S AI agents necessitates more sophisticated and interpretable evaluation metrics for conversational prosody and rhythm, which this research aims to address.
Improved evaluation directly informs the development and deployment of more human-like and effective conversational AI, which is critical for their societal integration and performance in complex interactions.
The ability to quantitatively and interpretably assess nuanced speech characteristics like F0, speaking rate, and rhythm will allow for more targeted improvements in generative speech models.
- · AI agent developers
- · Speech synthesis researchers
- · End-users of conversational AI
- · Language model companies
- · AI models with poor prosodic control
- · Companies relying on simplistic speech evaluation metrics
More natural and emotionally intelligent speech-to-speech AI agents become a tangible near-term reality.
Improved conversational AI enables entirely new applications requiring nuanced human-AI interaction, such as therapy bots or advanced customer service.
The benchmark for human-AI interaction rises, increasing user expectations and potentially accelerating the widespread adoption of AI agents across various sectors.
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Read at arXiv cs.CL