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Publication
Automatic evaluation of reading aloud performance in children
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Advisor(s)
Abstract(s)
Evaluating children’s reading aloud proficiency is typically a task done by teachers on an individual ba sis, where reading time and wrong words are marked manually. A computational tool that assists with recording reading tasks, automatically analyzing them and outputting performance related metrics could be a significant help to teachers. Working towards that goal, this work presents an approach to automat ically predict the overall reading aloud ability of primary school children by employing automatic speech processing methods. Reading tasks were designed focused on sentences and pseudowords, so as to obtain complementary information from the two distinct assignments. A dataset was collected with recordings of 284 children aged 6–10 years reading in native European Portuguese. The most common disfluencies identified include intra-word pauses, phonetic extensions, false starts, repetitions, and mispronunciations. To automatically detect reading disfluencies, we first target extra events by employing task-specific lat tices for decoding that allow syllable-based false starts as well as repetitions of words and sequences
of words. Then, mispronunciations are detected based on the log likelihood ratio between the recognized and target words. The opinions of primary school teachers were gathered as ground truth of overall read ing aloud performance, who provided 0–5 scores closely related to the expected performance at the end of each grade. To predict these scores, various features were extracted by automatic annotation and re gression models were trained. Gaussian process regression proved to be the most successful approach. Feature selection from both sentence and pseudoword tasks give the closest predictions, with a correla tion of 0.944 compared to the teachers’ grading. Compared to the use of manual annotation, where the best models obtained give a correlation of 0.949, there was a relative decrease of only 0.5% for using automatic annotations to extract features. The error rate of predicted scores relative to ground truth also
proved to be smaller than the deviation of evaluators’ opinion per child.
Description
Keywords
Reading level assessment Child speech Pseudoword reading Disfluency detection Gaussian process regression
Citation
Jorge Proença, Carla Lopes, Michael Tjalve, Andreas Stolcke, Sara Candeias, Fernando Perdigão, Automatic evaluation of reading aloud performance in children, Speech Communication, Volume 94, 2017, Pages 1-14, ISSN 0167-6393, https://doi.org/10.1016/j.specom.2017.08.006
Publisher
Elsevier BV
Collections
CC License
Without CC licence