Abstract
As online automatic speech recognition (ASR) engines become more accurate and more widely implemented with CALL software, it becomes important to evaluate the effectiveness and the accuracy of these recognition engines using authentic speech samples. This study investigates two of the most prominent cloud-based speech recognition engines- Apple’s Siri and Google Speech Recognition (GSR) to determine which engine would be more accurate at transcribing L2 learners’ speech. The average recognition accuracy of Siri and GSR is reported using language samples of Japanese learners speaking English. The study also presents a series of computerized speech assessment tasks that were developed by the researchers using a cloud-based speech recognition engine in conjunction with Moodle, a widely used course management system.
Highlights
Background of speech recognitionComputerized speech recognition systems were being designed as far back as the early 1930s when Bell Labs began conducting research on computerized transcription of human speech
While speech recognition initially was lauded as an effective text input method, users unsurprisingly preferred keyboards to microphones for text input
As the accuracy and the efficiency of speech recognition software improve, a wider range of user may embrace it. It was not long before language educators and call developers became interested in integrating speech recognition technology with call activities, with language production practice
Summary
Background of speech recognitionComputerized speech recognition systems were being designed as far back as the early 1930s when Bell Labs began conducting research on computerized transcription of human speech. As the accuracy and the efficiency of speech recognition software improve, a wider range of user may embrace it. It was not long before language educators and call developers became interested in integrating speech recognition technology with call activities, with language production practice. Studies conducted by Neri, et al (2002), Ploger (2015), Hincks (2002), and Elimat & AbuSeileek (2014) suggest that asr holds potential benefits for language learners, when coupled with self-study call activities that incorporate practical learner feedback. Neri, et al (2002) observed that pronunciation training using asr offered a valuable, stress-free learner experience, when learners were provided verification of correct responses as well as effective remedies for their learning errors. The importance of immediate and useful feedback is a recurrent theme and a feature which needs to be given careful consideration when designing asr activities for language learning purposes
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