CN-NewsTTS Bench: a target-level automatic benchmark for raw-input Chinese news TTS pronunciation
arXiv:2606.24714v1 Announce Type: new Abstract: Chinese news text contains dense written forms such as scores, hyphenated model names, ranges, unit symbols, percentages, English abbreviations, and mixed Chinese-Latin-digit names. These forms are frequent in real listening workflows, and a text-to-speech (TTS) system can preserve the written string while changing the spoken meaning. We introduce CN-NewsTTS Bench v0.1, an open target-level benchmark for evaluating whether Chinese news TTS products pronounce such targets correctly from raw text, without user-side rules, LLM rewriting, SSML hints,
The proliferation of advanced AI includes TTS, which requires increasingly robust evaluation methods for real-world application, especially for complex languages like Chinese.
This benchmark addresses a critical gap in assessing TTS system performance for nuanced language features, directly impacting the quality and reliability of AI-generated spoken content.
The introduction of a standardized, open benchmark for Chinese news TTS allows for more objective and comparable evaluations of pronunciation accuracy, particularly for challenging text forms.
- · TTS developers focusing on accuracy
- · Companies requiring high-fidelity spoken AI output
- · Users of Chinese AI voice services
- · TTS systems with poor handling of complex written forms
- · Benchmarks that lack granular pronunciation evaluation
Improved performance of Chinese TTS systems in handling complex linguistic structures.
Increased adoption of AI voice assistants and automated news readers in Chinese-speaking markets due to higher reliability.
Potential for new business models built on highly accurate, context-aware AI voice generation for specialized content.
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Read at arXiv cs.CL