Neural coding in the auditory system has been shown to obey the principle of efficient neural coding. The statistical properties of speech appear to be particularly well matched to the auditory neural code. However, only English has so far been analyzed from an efficient coding perspective. It thus remains unknown whether such an approach is able to capture differences between the sound patterns of different languages. Here, we use independent component analysis to derive information theoretically optimal, non-redundant codes (filter populations) for seven typologically distinct languages (Dutch, English, Japanese, Marathi, Polish, Spanish and Turkish) and relate the statistical properties of these filter populations to documented differences in the speech rhythms () and consonant inventories () of these languages. We show that consonant class membership plays a particularly important role in shaping the statistical structure of speech in different languages, suggesting that acoustic )