Last summer, computer science professor Julie Medero, along with Alfredo Gomez \u201921, Alicia Ngo \u201920 and Ali Otondo \u201920 (aka The A Team), embarked on a project to develop an iOS application that would help children improve their reading skills. The resulting research paper, \u201cReading KiTTY: Pitch Range as an Indicator of Reading Skill,\u201d has been accepted to the Widening Natural Language Processing (WiNLP) workshop, to be held during the Association for Computational Linguistics conference later this summer in Florence, Italy.<\/p>\n
\u201cThis paper is about an analysis of the prosody of children\u2019s oral reading,\u201d says Medero (prosody refers to the patterns of rhythm and sound in text, e.g., pitch, reading speed, emotionality). \u201cThat means we\u2019re looking at how children of different reading levels use their voices as part of reading out loud. Our lab\u2019s Reading KiTTY project is looking at how elementary-aged children could be guided through the creation of kinetic typography animations that visualize their reading out loud. Kinetic typography is a form of animation that uses size, color and motion of text, along with images, to represent the meaning of a text. It\u2019s popular in music videos and is also commonly used in videos of famous speeches, but we think it has the potential for interesting applications in literacy education, too.\u201d<\/p>\n
\u201cThe research focuses on two aspects,\u201d says Gomez, who will make a presentation at the workshop, \u201ccreating the first iteration of the app and exploring how we could leverage natural language processing and speech processing in order effectively promote creativity. The paper accepted by WiNLP focuses on our work using pitch range as an indicator for reading skill as we apply\u00a0machine learning and other computational linguistics techniques.\u201d<\/p>\n
Ngo explains how the app works: \u201cFirst, children read aloud for the app.\u00a0After they finish reading, children can see their words come to life through kinetic typography. For example, if a child reads a question prosodically, the kinetic typography should show a pitch rise at the end of the sentence, perhaps by positioning the high-pitched letters higher (along the vertical axis) than the other letters. Using our results, we intend to provide teachers with unique feedback of each student\u2019s prosody, and therefore, each student\u2019s reading comprehension.\u201d<\/p>\n
Ngo continues, \u201cAlfredo and I trained a machine-learning model with scikit-learn to predict the presence of a high pitch in a text, similar to developing text-to-speech software. After realizing that was really difficult (Google hasn\u2019t even figured this one out completely yet), we decided to conduct some more data analyses to understand our dataset. We analyzed and extracted data from 5,000+ audio files of children reading, using Natural Language Toolkit and NLP software (AuToBI and Praat). We found that there is a statistically significant\u00a0difference\u00a0in the average pitch range between\u00a0skilled\u00a0readers and struggling readers as they read\u00a0sentences.\u201d<\/p>\n
The researchers hope to add more ways to gauge a student\u2019s reading comprehension to include reading speed, emotionality\/enthusiasm and the reader\u2019s pause lengths between words.<\/p>\n
\u201cThis project is important because teachers often don\u2019t have the time to listen to each individual student read,\u201d says Ngo. \u201cReading KiTTY can provide unique feedback for each student, so that teachers can focus on\u00a0improving\u00a0their students\u2019 reading comprehension instead of merely\u00a0measuring\u00a0their students\u2019 reading comprehension.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"
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