When comparing speech sounds across languages, scholars often make use of feature representations of individual sounds in order to determine fine-grained sound similarities. Although binary feature systems for large numbers of speech sounds have been proposed, large-scale computational applications often face the challenges that the proposed feature systems -- even if they list features for several thousand sounds -- only cover a smaller part of the numerous speech sounds reflected in actual cross-linguistic data. In order to address the problem of missing data for attested speech sounds, we propose a new approach that can create binary feature vectors dynamically for all sounds that can be represented in the the standardized version of the International Phonetic Alphabet proposed by the Cross-Linguistic Transcription Systems (CLTS) reference catalog. Since CLTS is actively used in large data collections, covering more than 2,000 distinct language varieties, our procedure for the generation of binary feature vectors provides immediate access to a very large collection of multilingual wordlists. Testing our feature system in different ways on different datasets proves that the system is not only useful to provide a straightforward means to compare the similarity of speech sounds, but also illustrates its potential to be used in future cross-linguistic machine learning applications.

翻译：在跨语言比较语音时，学者常利用单个语音的特征表示来确定精细的语音相似性。尽管已有针对大量语音的二元特征系统被提出，但大规模计算应用往往面临这样的挑战：即便是列出了数千个语音特征的现有系统，也只能覆盖实际跨语言数据中所反映的众多语音的一小部分。为解决已知语音数据缺失的问题，我们提出了一种新方法，该方法能够为所有可以用跨语言转录系统（CLTS）参考目录所提出的标准化国际音标表示的语音动态生成二元特征向量。由于CLTS被广泛应用于涵盖2000多种不同语言变体的大型数据集中，我们提出的二元特征向量生成程序可即时访问海量多语言词表。通过在多个数据集上以不同方式测试本特征系统，证明该系统不仅能为比较语音相似性提供直观工具，更彰显了其在未来跨语言机器学习应用中的潜力。