A new study published on arXiv explores the human-likeness of speech-to-speech (S2S) systems, finding that current models fail to pass a preliminary Turing test. Researchers collected human judgments on dialogues involving nine S2S systems and 28 human participants, revealing that the primary limitations lie in paralinguistic features and emotional expressivity rather than semantic understanding. The study also developed an interpretable model for automatic human-likeness evaluation, aiming to guide future improvements in conversational AI. AI
IMPACT Highlights limitations in current conversational AI, particularly in paralinguistic and emotional expression, guiding future research.
RANK_REASON The cluster contains two academic papers discussing AI speech capabilities and evaluation.
- algorithmic speech
- alphaXiv
- arXiv
- CatalyzeX Code Finder for Papers
- DagsHub
- Gao Jiabao
- Gotit.pub
- Hugging Face
- Machine Speech
- ScienceCast
- Speech-to-Speech (S2S) systems
- Turing test
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