Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin
Phonetic forced alignment is a key technique in phonetic research, yet existing alignment systems lack specialized models for low-resource language varieties. We address this by training text-dependent and text-independent aligners for Chengdu Mandarin using a 17-hour corpus and a custom G2P dictionary. We trained a text-dependent GMM-HMM model (Chengdu-MFA) and fine-tuned a pretrained audio encoder on frame classification with Chengdu-MFA's pseudo label for text-independent alignment (Chengdu-FC). Evaluation on an expert-annotated test set show that both methods significantly outperform Stand
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- LinkedLinked via arxiv author · 85%Zhiheng Qian →
“Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin”
- LinkedLinked via arxiv author · 85%Aini Li →
“Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin”
- LinkedLinked via arxiv author · 85%Dahai Hu →
“Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin”
- LinkedLinked via arxiv author · 85%Liang Zhao →
“Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin”