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Literacy assessment is an important activity for education administratorsacross the globe. Typically achieved in a school setting by testing a child'soral reading, it is intensive in human resources. While automatic speechrecognition (ASR) is a potential solution to the problem, it tends to becomputationally expensive for hand-held devices apart from needing language andaccent-specific speech for training. In this work, we propose a system topredict the word-decoding skills of a student based on simple acoustic featuresderived from the recording. We first identify a meaningful categorization ofword-decoding skills by analyzing a manually transcribed data set of children'soral reading recordings. Next the automatic prediction of the category isattempted with the proposed acoustic features. Pause statistics, syllable rateand spectral and intensity dynamics are found to be reliable indicators ofspecific types of oral reading deficits, providing useful feedback bydiscriminating the different characteristics of beginning readers. Thiscomputationally simple and language-agnostic approach is found to provide aperformance close to that obtained using a language dependent ASR that requiredconsiderable tuning of its parameters.

Predicting lexical skills from oral reading with acoustic measures