Integrating Automatic Speech Recognition Technology Into Vocabulary Learning in a Flipped English Class for Chinese College Students

Abstract

Although the automatic speech recognition (ASR) technology is increasingly used for commercial purposes, its impact on language learning has not been extensively studied. Underpinned by the sociocultural theory, the present work examined the effects of leveraging ASR technology to support English vocabulary learning in a tertiary flipped setting. A control group and an experimental group of college students participated in a 14-week study. Both groups had their English classes in a flipped fashion, but the experimental group was assigned with ASR-assisted oral tasks for pre-class self-learning. The pre- and post-intervention in-class task performance of both groups was audio-recorded and transcribed for data analysis. The triadic complexity-accuracy-fluency (CAF) framework was adopted to evaluate the participants' vocabulary learning. The between- and within-subjects effects were examined mainly through procedures of MANCOVA and mixed-design repeated measures ANCOVA. Results showed that on all the metrics of lexical complexity and speed fluency, the experimental group outperformed the control group, and had significant growth over time. On the other hand, the control group only improved significantly overtime on the G-index. On lexical accuracy, there was no significant difference between the two groups, and the within-subjects effect was not significant for either group. The findings lent some support to Skehan's Trade-off Hypothesis and discussions were conducted regarding the triarchic CAF framework.