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This study investigates whether contextualized embeddings from a large language model predict spoken word duration and pitch contours for Mandarin monosyllabic words, demonstrating above-chance prediction and the ability to back-transform normalized f0 contours to ms time scale.
This paper proposes a reference-based evaluation protocol for assessing prosody and rhythm in speech-to-speech AI systems, using matched human conversation data to provide interpretable behavioral plausibility checks.
Introduces MMEE, a multilingual multi-emotion emphasis corpus of 10,000 utterances across 7 languages and 34 emotions, and benchmarks emphasis detection models under various transfer settings, finding that multilingual training improves robustness while monolingual models show limited zero-shot transfer.
A survey of automated presentation coaching systems that reviews existing systems, introduces a five-dimensional task taxonomy covering pronunciation, stress, prosody, pacing, and content faithfulness, and identifies open challenges such as annotation scarcity, accent fairness, and low-latency feedback.
Proposes TextPro-SLM, a speech large language model that minimizes the modality gap by processing spoken input to resemble prosody-aware text input, achieving strong paralinguistic understanding with low training data.