论文标题
濒危和灭绝的萨摩西语语言的语音识别
Speech Recognition for Endangered and Extinct Samoyedic languages
论文作者
论文摘要
我们的研究提出了一系列有关言语识别的实验，该实验在西伯利亚北部和南部所说的濒临灭绝和灭绝的Samoyedic语言。据我们所知，这是第一次为灭绝的语言构建功能性的ASR系统。我们使用Kamas语言实现了15 \％的标签错误率，并通过仔细的错误分析得出结论，该质量已经非常有用，作为精制人类转录的起点。我们使用相关Nganasan语言的结果更为适中，最佳模型的错误率为33 \％。但是，我们通过逐步扩大KAMAS培训数据的实验表明，Nganasan的结果符合语言低资源情况下的预期。基于此，我们为场景提供了建议，其中进一步的语言文档或存档处理活动可以从现代ASR技术中受益。所有培训数据和处理脚本都没有在Zenodo上发布，并获得了明确的许可，以确保在此重要主题中进一步工作。
Our study presents a series of experiments on speech recognition with endangered and extinct Samoyedic languages, spoken in Northern and Southern Siberia. To best of our knowledge, this is the first time a functional ASR system is built for an extinct language. We achieve with Kamas language a Label Error Rate of 15\%, and conclude through careful error analysis that this quality is already very useful as a starting point for refined human transcriptions. Our results with related Nganasan language are more modest, with best model having the error rate of 33\%. We show, however, through experiments where Kamas training data is enlarged incrementally, that Nganasan results are in line with what is expected under low-resource circumstances of the language. Based on this, we provide recommendations for scenarios in which further language documentation or archive processing activities could benefit from modern ASR technology. All training data and processing scripts haven been published on Zenodo with clear licences to ensure further work in this important topic.
