  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.
