Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition
  and Phoneme to Grapheme Translation
Wonjun Lee Gary Geunbae Lee Yunsu Kim
This research optimizes two-pass cross-lingual transfer learning in
low-resource languages by enhancing phoneme recognition and phoneme-to-grapheme
translation models. Our approach optimizes these two stages to improve speech
recognition across languages. We optimize phoneme vocabulary coverage by
merging phonemes based on shared articulatory characteristics, thus improving
recognition accuracy. Additionally, we introduce a global phoneme noise
generator for realistic ASR noise during phoneme-to-grapheme training to reduce
error propagation. Experiments on the CommonVoice 12.0 dataset show significant
reductions in Word Error Rate (WER) for low-resource languages, highlighting
the effectiveness of our approach. This research contributes to the
advancements of two-pass ASR systems in low-resource languages, offering the
potential for improved cross-lingual transfer learning.