While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community. In this paper, we address the detection of sound change through historical spelling. We propose that a sound change can be captured by comparing the relative distance through time between their distributions using PPMI character embeddings. We verify this hypothesis in synthetic data and then test the method's ability to trace the well-known historical change of lenition of plosives in Danish historical sources. We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared. The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distribution.

本文针对语言变化的另一方面——声音变化进行了研究，提出了一种可以通过比较不同时期的分布距离来捕捉声音变化的理论，并通过PPMI字符嵌入比较并验证了这一理论，在丹麦历史文献中成功找到了若干个相关的语音变化。该方法可以用于探究一些未解决的问题。

历史声音变化建模：通过历时字符嵌入 Letters From the Past