Recent advances in neural text-to-speech (TTS) and multilingual speech generation have substantially improved synthetic speech quality, yet these gains remain unevenly distributed across the world's languages. Existing models are still dominated by a small set of high-resource languages, while many studies of low-resource TTS are simulated on artificially downsampled high-resource corpora that do not reflect the orthographic variation and limited phonetic coverage encountered in genuinely underrepresented settings. As such, we introduce OpenBibleTTS, which is a large-scale benchmark for low-resource speech synthesis spanning 37 underrepresented languages. Moreover, a systematic comparison of various TTS architectures and large-scale speech generation models is conducted across in-domain Biblical text and out-of-domain material. Results show that no single system dominates across languages and metrics: Gemini-TTS achieves the highest listener ratings on most evaluated languages, but monolingual EveryVoice models trained on OpenBibleTTS remain strongest for intelligibility and are preferred in several African languages, while open from-scratch systems degrade sharply on out-of-domain text, revealing a persistent gap between broad multilingual coverage and reliable synthesis quality in underserved linguistic communities. We complement automatic evaluation with subjective human judgments, and open-source all processed datasets, alignments, and trained models to support future low-resource TTS research.

翻译：神经文本转语音（TTS）与多语言语音生成的最新进展显著提升了合成语音质量，但这些成果在全球语言中的分布仍不均衡。现有模型仍由少数高资源语言主导，而许多低资源TTS研究基于对高资源语料库进行人为降采样处理后的仿真数据，未能反映真实低资源场景中存在的正字法差异与有限音系覆盖问题。为此，我们提出OpenBibleTTS——覆盖37种低资源语言的大规模低资源语音合成基准。进一步，我们在领域内圣经文本与领域外材料上系统比较了多种TTS架构与大尺度语音生成模型。结果表明，没有任何单一系统能在所有语言与指标上占据优势：Gemini-TTS在大多数被评估语言中获得最高听众评分，但基于OpenBibleTTS训练的单一语言EveryVoice模型在可懂度上仍表现最佳，且在多种非洲语言中更受青睐；而开源从头训练系统在领域外文本上性能急剧下降，揭示出广泛多语言覆盖与弱势语言社区可靠合成质量之间持续存在的差距。我们以主观人工评价补充自动评估，并开源全部已处理数据集、对齐结果与训练模型，以支持未来低资源TTS研究。