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arXiv · 2510.26190

SP-MCQA: Evaluating Intelligibility of TTS Beyond the Word Level

Hitomi Jin Ling Tee·Chaoren Wang·Zijie Zhang·Zhizheng Wu
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Abstract

The evaluation of intelligibility for TTS has reached a bottleneck, as existing assessments heavily rely on word-by-word accuracy metrics such as WER, which fail to capture the complexity of real-world speech or reflect human comprehension needs. To address this, we propose Spoken-Passage Multiple-Choice Question Answering, a novel subjective approach evaluating the accuracy of key information in synthesized speech, and release SP-MCQA-Eval, an 8.76-hour news-style benchmark dataset for SP-MCQA evaluation. Our experiments reveal that low WER does not necessarily guarantee high key-information accuracy, exposing a gap between traditional metrics and practical intelligibility. SP-MCQA shows that even state-of-the-art (SOTA) models still lack robust text normalization and phonetic accuracy. This work underscores the urgent need for high-level, more life-like evaluation criteria now that many systems already excel at WER yet may fall short on real-world intelligibility.

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Hitomi Jin Ling Tee, Chaoren Wang, Zijie Zhang, Zhizheng Wu. 2025-10-30. SP-MCQA: Evaluating Intelligibility of TTS Beyond the Word Level. https://arxiv.org/abs/2510.26190

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