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ProPS can generate speaker embeddings that accurately reflect complex attributes from simple natural language prompts, revolutionizing how we synthesize speaker identities.
Current speech-to-speech models may sound good, but they miss the mark on natural conversational dynamics, revealing critical areas for improvement.
Matched reference regimes for prosody evaluation reveal that traditional methods over-flag deviations, leading to misinterpretations in speech AI assessments.
Emotional entrainment detection can reach 97.01% accuracy by considering the temporal dynamics of dyadic speech interactions.
LLMs can spot fake words in speech by recognizing common editing patterns, but this reliance on learned biases hinders generalization to new manipulation techniques.
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.
