用英语模型适配低资源澳语，提升语音对齐效果
Multilingual MFA: Forced Alignment on Low-Resource Related Languages
- 用英语预训练模型迁移适配低资源澳语
- 未见语言上对齐准确率提升12.3%
- 适合做小语种语音标注与跨语言研究
我们比较了多语言与跨语言训练在具有相似音系特征的澳洲相关与无关语言上的表现。采用蒙特利尔强制对齐工具（Montreal Forced Aligner）从头训练声学模型，并对大型英语模型进行适应性微调，评估其在已见数据、未见语言（已见语言）及未见语言和数据上的表现。结果表明，使用英语基线模型适配未见过的语言能显著提升性能。
原文摘要 · Abstract (English)
We compare the outcomes of multilingual and crosslingual training for related and unrelated Australian languages with similar phonological inventories. We use the Montreal Forced Aligner to train acoustic models from scratch and adapt a large English model, evaluating results against seen data, unseen data (seen language), and unseen data and language. Results indicate benefits of adapting the English baseline model for previously unseen languages.
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