Sanskrit /n/-retroflexion is one of the most complex segmental processes in phonology. While it is still star-free, it does not fit in any of the subregular classes that are commonly entertained in the literature. We show that when construed as a phonotactic dependency, the process fits into a class we call inputoutput tier-based strictly local (IO-TSL), a natural extension of the familiar class TSL. IO-TSL increases the power of TSL's tier projection function by making it an input-output strictly local transduction. Assuming that /n/retroflexion represents the upper bound on the complexity of segmental phonology, this shows that all of segmental phonology can be captured by combining the intuitive notion of tiers with the independently motivated machinery of strictly local mappings.

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This paper looks at the case of so-called neutral roots in Uyghur (Turkic: China), whose idiosyncratic behavior with respect to the backness harmony system has been analyzed as stemming from a covert vowel contrast. Based on considerations of the structural properties of the language and the results of an experimental study, we suggest that an analysis based on lexical exceptionality is more parsimonious than the traditional analysis, unifying the treatment of neutral roots with other cases of exceptionality in the harmony system and accounting for a relationship between the patterning of roots and their frequency. We close by discussing implications for covert contrast analyses in general.

An important question in phonology is to what degree the learner uses distributional information rather than substantive properties of speech sounds when learning phonological structure. This paper presents an algorithm that learns phonological classes from only distributional information: the contexts in which sounds occur. The input is a segmental corpus, and the output is a set of phonological classes. The algorithm is first tested on an artificial language, with both overlapping and nested classes reflected in the distribution, and retrieves the expected classes, performing well as distributional noise is added. It is then tested on four natural languages. It distinguishes between consonants and vowels in all cases, and finds more detailed, language-specific structure. These results improve on past approaches, and are encouraging, given the paucity of the input. More refined models may provide additional insight into which phonological classes are apparent from the distributions of sounds in natural languages.

Keven & Akins suggest that innate stereotypies like TP/R may participate in the acquisition of tongue control. This commentary examines this claim in the context of speech motor learning and biomechanics, proposing that stereotypies could provide a basis for both swallowing and speech movements, and provides biomechanical simulation results to supplement neurological evidence for similarities between the two behaviors.