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Speech Enabled Reading Fluency Assessment: a Validation Study
Max van der Velde1,2, Wieke Harmsen3, Bernard P Veldkamp1
1Cognition, Data and Education Section, Faculty of BMS, University of Twente, De Zul 10, Enschede, NJ 7522 The Netherlands.
This study shows that automatic speech recognition (ASR) can accurately assess oral reading fluency in Dutch, a semi-transparent language. This AI-based tool offers a valid and efficient alternative for measuring reading skills in primary education.
Area of Science:
- Educational Technology
- Computational Linguistics
- Psycholinguistics
Background:
- Reading comprehension is crucial for societal function, yet student skills are declining globally.
- Oral reading fluency (accuracy, speed, automaticity, prosody) is a prerequisite for reading comprehension.
- Current fluency assessments are burdensome and lack diagnostic detail; AI offers potential solutions but requires validation.
Purpose of the Study:
- To evaluate the validity of an automatic speech recognition (ASR)-based instrument for assessing oral reading fluency in a semi-transparent language.
- To determine if ASR can generate valid measures of word decoding and passage reading (accuracy, speed, automaticity).
Main Methods:
- A validation study using the Argument-Based Approach to Validation.
- Analysis of 176 hours of speech data from 653 Dutch primary school children (grades 2-3).
- Comparison with 569 word-reading and 622 passage-reading tests.
Main Results:
- The ASR-based instrument successfully generated valid fluency metrics (accuracy, speed, automaticity) for Dutch.
- The AI approach demonstrated feasibility for assessing reading fluency in semi-transparent languages.
Conclusions:
- ASR-based assessment is a viable method for measuring oral reading fluency in semi-transparent languages.
- Future work should focus on optimizing ASR, error analysis, and incorporating prosody for a comprehensive assessment tool.
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