  Studying the neurological, genetic and evolutionary basis of human vocal
communication mechanisms using animal vocalization models is an important field
of neuroscience. The data sets typically comprise structured sequences of
syllables or `songs' produced by animals from different genotypes under
different social contexts. We develop a novel Bayesian semiparametric framework
for inference in such data sets. Our approach is built on a novel class of
mixed effects Markov transition models for the songs that accommodates
exogenous influences of genotype and context as well as animal-specific
heterogeneity. We design efficient Markov chain Monte Carlo algorithms for
posterior computation. Crucial advantages of the proposed approach include its
ability to provide insights into key scientific queries related to global and
local influences of the exogenous predictors on the transition dynamics via
automated tests of hypotheses. The methodology is illustrated using simulation
experiments and the aforementioned motivating application in neuroscience.
