Exploring automatic speech recognition for corrective and confirmative pronunciation feedback

Abstract Given that second language pronunciation errors are typically variable, learners would benefit from feedback that both flags errors ( corrective feedback ) and confirms correct pronunciation ( confirmative feedback ). We investigated Google Translate (GT) automatic speech recognition (ASR) transcription accuracy to determine its capacity to provide such feedback, based on Quebec francophone recordings of correctly/incorrectly realized English th-initial, h-initial and vowel-initial items in predictable/unpredictable sentence contexts. Recordings from male and female speakers were used to verify possible gender bias. In predictable contexts, transcription accuracy rates were higher for correct vs incorrect pronunciations; rates in unpredictable contexts for correct or incorrect pronunciations fell midway between the two. GT ASR is thus better at providing confirmative feedback in predictable contexts but corrective feedback in unpredictable contexts. Regardless of context, accuracy was considerably higher on errors leading to real-word than nonword output. Contra the anticipated pattern, female speakers were transcribed with higher accuracy than male.