Compositional Phoneme Approximation for L1-Grounded L2 Pronunciation Training
This addresses pronunciation training for second language learners by leveraging native-language phonemes, offering a more efficient approach compared to conventional methods.
The paper tackled the problem of second language (L2) pronunciation training by proposing an L1-grounded method using compositional phoneme approximation, which achieved a 76% in-box formant rate, 17.6% relative improvement in phoneme recognition accuracy, and over 80% of speech rated as more native-like in evaluations with Korean learners of English.
Learners of a second language (L2) often map non-native phonemes to similar native-language (L1) phonemes, making conventional L2-focused training slow and effortful. To address this, we propose an L1-grounded pronunciation training method based on compositional phoneme approximation (CPA), a feature-based representation technique that approximates L2 sounds with sequences of L1 phonemes. Evaluations with 20 Korean non-native English speakers show that CPA-based training achieves a 76% in-box formant rate in acoustic analysis, 17.6% relative improvement in phoneme recognition accuracy, and over 80% of speech being rated as more native-like, with minimal training. Project page: https://gsanpark.github.io/CPA-Pronunciation.