Is Natural Always Appropriate? Investigating Naturalness and Appropriateness Across Different Domains for TTS Evaluation
arXiv:2606.31729v1 Announce Type: cross Abstract: Text-to-speech (TTS) evaluation is an open challenge. While the primary target was "naturalness," recent fidelity gains shifted focus toward "appropriateness" and whether speech is correct for its context. In this work, we examine how perception changes when the expected downstream use varies. We measure the appropriateness and human-likeness of five SOTA TTS systems across five domains: AI assistant, reader, actor, animated character, and spontaneous speaker. Results show appropriateness varies across domains independently of naturalness. Whil
The rapid advancements in TTS fidelity necessitate more nuanced evaluation metrics beyond simple naturalness, pushing research towards context-aware appropriateness.
Sophisticated TTS systems require evaluation methods that can differentiate utility across diverse applications, moving beyond a single metric of naturalness to context-specific appropriateness.
TTS evaluation is shifting from a uniform 'naturalness' standard to a multi-faceted assessment of 'appropriateness' based on domain-specific usage, indicating a maturation of the field.
- · AI assistant developers
- · Entertainment industry (actors, animators)
- · TTS research institutions
- · Context-aware AI applications
- · Generic TTS evaluation metrics
- · Simple naturalness-focused TTS models
TTS models will be optimized for specific domain appropriateness rather than general human-likeness.
This specialization will lead to more effective and user-satisfying TTS deployments in practical applications like AI assistants and content creation.
The ability to generate highly context-appropriate synthetic speech could enable new forms of automated media production and personalized digital interactions.
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Read at arXiv cs.LG