This dissertation proposes a model of phonological relationships, the
Probabilistic Phonological Relationship Model (PPRM), that quantifies how
predictably distributed two sounds in a relationship are. It builds on a
core premise of traditional phonological analysis, that the ability to
define phonological relationships such as contrast and allophony is crucial
to the determination of phonological patterns in language.
The PPRM starts with one of the long-standing tools for determining
phonological relationships, the notion of predictability of distribution.
Building on insights from probability and information theory, the model
provides a way of calculating the precise degree to which two sounds are
predictably distributed, rather than maintaining the traditional binary
distinction between 'predictable' and 'not predictable.' It includes a
measure of the probability of each member of a pair in each environment
they occur in, the uncertainty (entropy) of the choice between the members
of the pair in each environment, and the overall uncertainty of choice
between the members of the pair in a language. These numbers provide a way
to formally describe and compare relationships that have heretofore been
treated as exceptions, ignored, relegated to alternative grammars, or
otherwise seen as problematic for traditional descriptions of phonology.
The PPRM provides a way for what have been labelled 'marginal contrasts,'
'quasi-allophones,' 'semi-phonemes,' and the like to be integrated into the
phonological system: There are phonological relationships that are neither
entirely predictable nor entirely unpredictable, but rather belong
somewhere in between these two extremes.
The model, being based on entropy, which can be used to understand the
cognitive function of uncertainty, provides insight into a number of
phenomena in synchronic phonological patterning, diachronic phonological
change, language acquition, and language processing.
Examples of how the model can be applied are provided for two languages,
Japanese and German, using large-scale corpora to calculate the
predictability of distribution of various pairs of sounds. An example of
how empirical evidence for one of the predictions of the model, that
entropy and perceptual distinctness are inversely related to each other,
could be obtained is also provided.
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