Language-specific Effects on Automatic Speech Recognition Errors for World Englishes
June Choe | Yiran Chen | May Pik Yu Chan | Aini Li | Xin Gao | Nicole Holliday
Proceedings of the 29th International Conference on Computational Linguistics
Despite recent advancements in automated speech recognition (ASR) technologies, reports of unequal performance across speakers of different demographic groups abound. At the same time, the focus on performance metrics such as the Word Error Rate (WER) in prior studies limit the specificity and scope of recommendations that can be offered for system engineering to overcome these challenges. The current study bridges this gap by investigating the performance of Otter’s automatic captioning system on native and non-native English speakers of different language background through a linguistic analysis of segment-level errors. By examining language-specific error profiles for vowels and consonants motivated by linguistic theory, we find that certain categories of errors can be predicted from the phonological structure of a speaker’s native language.