CN-NewsTTS Bench: a target-level automatic benchmark for raw-input Chinese news TTS pronunciation
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Computer Science > Computation and Language
Title:CN-NewsTTS Bench: a target-level automatic benchmark for raw-input Chinese news TTS pronunciation
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, or manual edits. The release contains a 200-record development set, an 800-record public test set, 992 public auto-evaluable targets, fixed transcripts from a three-ASR ensemble, an automatic target scorer, and initial results for seven product TTS systems. We additionally report ASR-route diagnostics, ASR-subset ablations, category-level results, confidence intervals, and provider configuration metadata. The best system reaches 0.879 strict accuracy, while several systems remain below 0.60.
| Comments: | 5 pages, 1 figure, 8 tables. ICASSP-style preprint |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2606.24714 [cs.CL] |
| (or arXiv:2606.24714v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.24714
arXiv-issued DOI via DataCite (pending registration)
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