Automatic Annotation of Disfluent Speech in Children’s Reading Tasks

The automatic evaluation of reading performance of children is an important alternative to any manual or 1-on-1 evaluation by teachers or tutors. To do this, it is necessary to detect several types of reading miscues. This work presents an approach to annotate reading speech while detecting false-starts, repetitions and mispronunciations, three of the most common disfluencies. Using speech data of 6–10 year old children reading sentences and pseudowords, we apply a two-step process: first, an automatic alignment is performed to get the best possible word-level segmentation and detect syllable based false-starts and word repetitions by using a strict FST (Finite State Transducer); then, words are classified as being mispronounced or not through a likelihood measure of pronunciation by using phone posterior probabilities estimated by a neural network. This work advances towards getting the amount and severity of disfluencies to provide a reading ability score computed from several sentence reading tasks.