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Maltese is often described as having a hybrid morphological system resulting from extensive contact between Semitic and Romance language varieties. Such a designation reflects an etymological divide as much as it does a larger tradition in the literature to consider concatenative and non-concatenative morphological patterns as distinct in the language architecture. Using a combination of computational modeling and information theoretic methods, we quantify the extent to which the phonology and etymology of a Maltese singular noun may predict the morphological process (affixal vs. templatic) as well as the specific plural allomorph (affix or template) relating a singular noun to its associated plural form(s) in the lexicon. The results indicate phonological pressures shape the organization of the Maltese lexicon with predictive power that extends beyond that of a word’s etymology, in line with analogical theories of language change in contact. 

Infants learn the sound categories of their language and adults successfully process the sounds they hear, even though sound categories often overlap in their acoustics. Most researchers agree that listeners use context to disambiguate overlapping categories. However, they differ in their ideas about how context is used. One idea is that listeners normalize out the systematic effects of context from the acoustics of a sound. Another idea is that contextual information may itself be an informative cue to category membership, due to patterns in the types of contexts that particular sounds occur in. We directly contrast these two ways of using context by applying each one to the test case of Japanese vowel length. We find that normalizing out contextual variability from the acoustics does not improve categorization, but using context in a top-down fashion does so substantially. This reveals a limitation of normalization in phonetic acquisition and processing and suggests that approaches that make use of top-down contextual information are promising to pursue. 

We describe a model which jointly performs word segmentation and induces vowel categories from formant values. Vowel induction performance improves slightly over a baseline model which does not segment; segmentation performance decreases slightly from a baseline using entirely symbolic input. Our high joint performance in this idealized setting implies that problems in unsupervised speech recognition reflect the phonetic variability of real speech sounds in context.