Jen Jordan
Kwansei Gakuin University
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Standard Presentation (25-minute) The Relationship between learner proficiency and formulaic language use more
Sun, Sep 17, 15:25-15:50 Asia/Tokyo
Lexical phrases, defined here as multi-word units (MWEs) used to organize text, provide the glue that holds learner essays together. These discourse marking phrases and other formulaic language have been shown to be particularly challenging to learners even at the highest levels (ciation). This presentation will discuss the results of an analysis of learners’ use of lexical phrases in essays and the relationship between MWE use and proficiency level. Lower-level learner writing has rarely been examined in the context of learner corpus research nor has an attempt been made to determine a profile of lexical phrase use for learners at different proficiency levels. As such, the research discussed here sought to determine the relationship between learners’ use of lexical phrases in their writing and their proficiency level as determined by the 1) their performance on the TOEFL and 2) their essay grades. The results indicated that lower-level learners used more lexical phrases to scaffold their writing. In early submissions, lower-level learners used a greater range of phrases as well, however this trend reversed with later submissions. Higher-level learners generally used phrases more appropriately.
Standard Presentation (25-minute) Automated Transcription and Measures of Lexical Diversity in L2 Spoken Texts more
Sun, Sep 17, 14:15-14:40 Asia/Tokyo
While there has been an increase in the use of transformer-based Automated Speech Recognition (ASR) models like OpenAI’s Whisper for transcribing L1 speech (e.g. Lin, 2023; Seyedi et al., 2022) using these models to transcribe speech produced by L2 English learners can be difficult due to factors such as pronunciation errors, disfluencies, and atypical grammatical constructions (Wang et al., 2021). The presenters’ previous research (Brooks & Jordan, 2023) showed that these issues can result in word error rates (WER) that are higher than those of a professional transcriber. This presentation builds on this previous research and examines the viability of using ASR programs to transcribe texts being used to investigate lexical diversity. This is done by investigating the differences in the lexical diversity scores of the automated version of 100 texts (50 presentations and 50 discussions) compared to the professionally transcribed versions of the same texts. Results show that the accuracy of ASR transcriptions comes close to professional transcriptions and that the areas where ASR is likely to make mistakes tend not to impact measures of lexical diversity. The presenters will highlight areas where ASR-based transcribers struggle and best practices for using models such as Whisper for transcribing L2 texts.
