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.

优化两阶段跨语言迁移学习在低资源语言中的应用，通过改进音素识别和音素到字素转换模型，优化了语音识别跨语言学习过程，合并共享发音特征的音素以增强识别准确性，引入全局音素噪声生成器降低错误传播。在 CommonVoice 12.0 数据集上的实验结果表明，对于低资源语言，我们的方法显著减少了单词误差率（WER），展示了方法的有效性，对于改进低资源语言中的两阶段语音识别系统，提供了潜在的跨语言迁移学习。