AnimeScore: New Framework for Evaluating Anime-Like Speech Characteristics
Researchers have developed AnimeScore, a preference-based framework for automatically evaluating anime-like voice characteristics using pairwise ranking rather than traditional subjective scoring methods. The study analyzed 15,000 pairwise judgments from 187 evaluators and found that anime-likeness is driven by controlled resonance shaping, prosodic continuity, and deliberate articulation rather than simple factors like high pitch. The framework achieves up to 90.8% accuracy with machine learning models and could serve as a reward signal for optimizing generative speech models.
Researchers have introduced AnimeScore, a new framework addressing the challenge of objectively evaluating anime-like speech styles, which previously relied on costly subjective judgments without standardized metrics. Unlike naturalness evaluation, anime-likeness lacks a shared absolute scale, making conventional Mean Opinion Score (MOS) protocols unreliable. The team collected 15,000 pairwise preference judgments from 187 evaluators with free-form descriptions and conducted acoustic analysis to identify key characteristics. Results show that perceived anime-likeness is driven by controlled resonance shaping, prosodic continuity, and deliberate articulation rather than simple heuristics such as high pitch. While handcrafted acoustic features reached a 69.3% accuracy ceiling, self-supervised learning-based ranking models achieved up to 90.8% AUC, providing a practical metric that can also serve as a reward signal for preference-based optimization of generative speech models.
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- arXiv cs.CLCenter
AnimeScore: A Preference-Based Dataset and Framework for Evaluating Anime-Like Speech Style
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