  Syllable detection is an important speech analysis task with applications in
speech rate estimation, word segmentation, and automatic prosody detection.
Based on the well understood acoustic correlates of speech articulation, it has
been realized by local peak picking on a frequency-weighted energy contour that
represents vowel sonority. While several of the analysis parameters are set
based on known speech signal properties, the selection of the
frequency-weighting coefficients and peak-picking threshold typically involves
heuristics, raising the possibility of data-based optimisation. In this work,
we consider the optimization of the parameters based on the direct minimization
of naturally arising task-specific objective functions. The resulting
non-convex cost function is minimized using a population-based search algorithm
to achieve a performance that exceeds previously published performance results
on the same corpus using a relatively low amount of labeled data. Further, the
optimisation of system parameters on a different corpus is shown to result in
an explainable change in the optimal values.
