Comparison of L2 Korean pronunciation error patterns from five L1
  backgrounds by using automatic phonetic transcription
Eun Jung Yeo Hyungshin Ryu Jooyoung Lee Sunhee Kim Minhwa Chung
This paper presents a large-scale analysis of L2 Korean pronunciation error
patterns from five different language backgrounds, Chinese, Vietnamese,
Japanese, Thai, and English, by using automatic phonetic transcription. For the
analysis, confusion matrices are generated for each L1, by aligning canonical
phone sequences and automatically transcribed phone sequences obtained from
fine-tuned Wav2Vec2 XLS-R phone recognizer. Each value in the confusion
matrices is compared to capture frequent common error patterns and to specify
patterns unique to a certain language background. Using the Foreign Speakers'
Voice Data of Korean for Artificial Intelligence Learning dataset, common error
pattern types are found to be (1) substitutions of aspirated or tense
consonants with plain consonants, (2) deletions of syllable-final consonants,
and (3) substitutions of diphthongs with monophthongs. On the other hand,
thirty-nine patterns including (1) syllable-final /l/ substitutions with /n/
for Vietnamese and (2) /\textturnm/ insertions for Japanese are discovered as
language-dependent.