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

2026-07-23

AI Summary

This study addresses the challenge of accurate phoneme alignment in low-resource dialects by focusing on Chengdu Mandarin. Leveraging 17 hours of speech data and a custom phonetic lexicon, the authors propose a bootstrapped training pipeline: first, a text-dependent GMM-HMM aligner (Chengdu-MFA) is developed, whose outputs are then used as pseudo-labels to fine-tune a pretrained audio encoder for text-independent, frame-level classification-based alignment (Chengdu-FC). Evaluated on an expert-annotated test set, Chengdu-MFA reduces phoneme boundary error by 31.8% relative to a Mandarin baseline, and Chengdu-FC further improves this reduction to 61.2%. These results demonstrate the effectiveness and novelty of the proposed approach for phoneme alignment in low-resource dialectal settings.

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.

Problem

• phonetic forced alignment
• low-resource language varieties
• Chengdu Mandarin
• phone boundary accuracy

Innovation

• forced alignment
• low-resource languages
• GMM-HMM
• frame classification
• bootstrapping pipeline