  Speech sounds of the languages all over the world show remarkable patterns of
cooccurrence. In this work, we attempt to automatically capture the patterns of
cooccurrence of the consonants across languages and at the same time figure out
the nature of the force leading to the emergence of such patterns. For this
purpose we define a weighted network where the consonants are the nodes and an
edge between two nodes (read consonants) signify their co-occurrence likelihood
over the consonant inventories. Through this network we identify communities of
consonants that essentially reflect their patterns of co-occurrence across
languages. We test the goodness of the communities and observe that the
constituent consonants frequently occur in such groups in real languages also.
Interestingly, the consonants forming these communities reflect strong
correlations in terms of their features, which indicate that the principle of
feature economy acts as a driving force towards community formation. In order
to measure the strength of this force we propose an information theoretic
definition of feature economy and show that indeed the feature economy
exhibited by the consonant communities are substantially better than those if
the consonant inventories had evolved just by chance.
