分析濒危语言达拉瓦尔与107种高资源语言的语音相似性，助力语音技术适配。

Australian Aboriginal languages are of significant cultural and linguistic value but remain severely underrepresented in modern speech AI systems. While state-of-the-art speech foundation models and automatic speech recognition excel in high-resource settings, they often struggle to generalize to low-resource languages, especially those lacking clean, annotated speech data. In this work, we collect and clean a speech dataset for Dharawal, a low-resource Australian Aboriginal language, by carefully sourcing and processing publicly available recordings. Using this dataset, we analyze the speech similarity between Dharawal and 107 high-resource languages using a pre-trained multilingual speech encoder. Our approach combines (1) misclassification rate analysis to assess language confusability, and (2) fine-grained similarity measurements using cosine similarity and Fréchet Inception Distance (FID) in the embedding space. Experimental results reveal that Dharawal shares strong speech similarity with languages such as Latin, Māori, Korean, Thai, and Welsh. These findings offer practical guidance for future transfer learning and model adaptation efforts, and underscore the importance of data collection and embedding-based analysis in supporting speech technologies for endangered language communities.