Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition
This addresses the performance gap in ASR for non-native speakers, specifically Korean learners, with an incremental approach.
The study tackled the challenge of limited non-native speech data for ASR by incorporating Korean L2 phonemes using articulatory features, improving ASR accuracy for Korean L2 speech by training only on L1 data and enhancing recognition for both L1 and L2 speech without trade-offs through fine-tuning.
The limited availability of non-native speech datasets presents a major challenge in automatic speech recognition (ASR) to narrow the performance gap between native and non-native speakers. To address this, the focus of this study is on the efficient incorporation of the L2 phonemes, which in this work refer to Korean phonemes, through articulatory feature analysis. This not only enables accurate modeling of pronunciation variants but also allows for the utilization of both native Korean and English speech datasets. We employ the lattice-free maximum mutual information (LF-MMI) objective in an end-to-end manner, to train the acoustic model to align and predict one of multiple pronunciation candidates. Experimental results show that the proposed method improves ASR accuracy for Korean L2 speech by training solely on L1 speech data. Furthermore, fine-tuning on L2 speech improves recognition accuracy for both L1 and L2 speech without performance trade-offs.