Assessing Google Translate ASR for feedback on L2 pronunciation errors in unpredictable sentence contexts

Abstract

[EN] Following previous research into predictable sentence contexts, this study assesses the pronunciation feedback provided by Google Translate’s (GT) Automatic Speech Recognition (ASR) in unpredictable contexts. We examined the accuracy of GT transcriptions for target items recorded by male and female Quebec Francophones (QFs). The items occurred in neutral carrier sentences such that no contextual cues help ASR identify the targets. Th-initial vs t-initial (thank-tank) and h-initial vs vowel-initial (heat-eat) items were used to investigate the potential for feedback on the QF errors of th-substitution, h-deletion, and h-epenthesis, comparing real-word (thank→tank) vs nonword output (thief→tief). As with predictable contexts in our previous research, we observed high transcription accuracy for real words only. Without contextual cues, accuracy rates were lower than in predictable contexts for correctly pronounced items but higher than for incorrect pronunciations constituting real words. Unpredictable contexts are thus inferior at confirming correct pronunciation (confirmative feedback) but superior at flagging real-word errors (corrective feedback). Contrary to the anticipated ASR gender bias, female recordings showed higher transcription accuracy than male recordings. Our findings both confirm the usefulness of GT’s ASR for generating pronunciation feedback and highlight the importance of context (predictable vs unpredictable) and lexical status (real vs nonword).