Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin

Abstract: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 Standard Mandarin baselines. Chengdu-MFA reduced average phone boundary differences by 31.8%, while Chengdu-FC achieved a 61.2% reduction. This work establishes a practical bootstrapping pipeline for developing accurate aligners for under-resourced varieties without labor- and time-intensive manual annotation.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2607.21332 [cs.CL]

(or arXiv:2607.21332v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.21332 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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