  Literacy assessment is an important activity for education administrators
across the globe. Typically achieved in a school setting by testing a child's
oral reading, it is intensive in human resources. While automatic speech
recognition (ASR) is a potential solution to the problem, it tends to be
computationally expensive for hand-held devices apart from needing language and
accent-specific speech for training. In this work, we propose a system to
predict the word-decoding skills of a student based on simple acoustic features
derived from the recording. We first identify a meaningful categorization of
word-decoding skills by analyzing a manually transcribed data set of children's
oral reading recordings. Next the automatic prediction of the category is
attempted with the proposed acoustic features. Pause statistics, syllable rate
and spectral and intensity dynamics are found to be reliable indicators of
specific types of oral reading deficits, providing useful feedback by
discriminating the different characteristics of beginning readers. This
computationally simple and language-agnostic approach is found to provide a
performance close to that obtained using a language dependent ASR that required
considerable tuning of its parameters.
