用轻量流匹配模型实现三种原住民语言的多语言语音合成
Developing multilingual speech synthesis system for Ojibwe, Mi'kmaq, and Maliseet
- 采用无注意力机制的轻量流匹配架构
- 三语联合训练在数据少时优于单语模型
- 强调社区参与的人类评估重要性
我们为北美三种原住民语言——奥吉布瓦语、米克马克语和马利塞特语，开发了轻量级流匹配多语言文本到语音系统。结果表明，在数据稀缺情况下，对三种类型相似的语言进行联合训练，可显著提升语音合成性能，优于单语模型。无注意力架构在性能上与自注意力架构相当，且内存效率更高。本研究不仅推动了低资源语言复兴的技术进展，也揭示了当前人类评估协议中存在的文化偏差，呼吁采用更以社区为中心的评估方法。
原文摘要 · Abstract (English)
We present lightweight flow matching multilingual text-to-speech (TTS) systems for Ojibwe, Mi'kmaq, and Maliseet, three Indigenous languages in North America. Our results show that training a multilingual TTS model on three typologically similar languages can improve the performance over monolingual models, especially when data are scarce. Attention-free architectures are highly competitive with self-attention architecture with higher memory efficiency. Our research not only advances technical development for the revitalization of low-resource languages but also highlights the cultural gap in human evaluation protocols, calling for a more community-centered approach to human evaluation.
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