濒危和灭绝的萨摩耶语的语音识别
Speech Recognition for Endangered and Extinct Samoyedic languages
摘要：
我们的研究提出了一系列的实验语音识别濒危和灭绝的萨莫耶德语言，讲在西伯利亚北部和南部。据我们所知，这是第一次为一种灭绝的语言构建功能性ASR系统。我们用Kamas语言实现了15%的标签出错率，并通过仔细的错误分析得出结论，这种质量已经非常有用，作为一个起点，为完善的人类翻译。我们的结果与相关的Nganasan语言更温和，最好的模型有出错率的33\%。然而，我们表明，通过实验，其中卡玛斯训练数据是逐步扩大，Nganasan的结果是符合预期的低资源环境下的语言。在此基础上，我们为进一步的语言文档或归档处理活动可以从现代ASR技术中受益的场景提供了建议。所有训练数据和处理脚本都已在Zenodo上发布，并有明确的许可证，以确保在这一重要主题上的进一步工作。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
