Abstract
This paper explores the extraction and analysis of prosodic features in children's Filipino speech for application in automated oral reading fluency assessment. Automatic syllabication was optimised in the context of children's Filipino read speech. Using the Children Filipino Speech Corpus, prosodic features were automatically extracted which were then classified according to human rater assessment of fluency. Analysis of variance showed that speech and articulation rates, pauses, syllable duration, and pitch can be used to classify children's oral reading fluency in Filipino into three levels, namely, independent, instructional and frustration. Using machine learning classification methods, fivefold cross-validation showed that speech rate, articulation rate and number of pauses can be used to predict oral reading fluency at 92%, 85% and 76% accuracy for 2, 3 and 4 levels of fluency classification, respectively. Pitch and syllable duration patterns were also characterised for the assessment of phrasing and expression between fluent and non-fluent readers.
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