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The University of Tokyo, Japan
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Reducing inter-utterance silence from 9.6 seconds to 0.3 seconds transforms the quality of real-time game commentary, making it feel more natural and engaging.
AI-generated music detectors reveal hidden vulnerabilities when tested against unseen generator sources, with token performance varying dramatically based on training data.
Speaker embeddings from foundation models often misalign with human perceptions of similarity, revealing critical gaps in current AI speech processing.
Forget fine-tuning: Prompting MLLMs with a dynamic interval-based decoding strategy lets them generate surprisingly human-like, pause-aware real-time game commentary.
