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ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers Using Phonetic Features
Author(s)
Lin, Ye Bhone
Aung, Thura
Thu, Ye Kyaw
Oo, Thazin Myint
Date Issued
January 1, 2025
Type
Conference Paper
Abstract
This paper investigates sequence-to-sequence Transformer models for automatic speech recognition (ASR) error correction in low-resource Burmese, focusing on different feature integration strategies including IPA and alignment information. To our knowledge, this is the first study addressing ASR error correction specifically for Burmese. We evaluate five ASR backbones and show that our ASR Error Correction (AEC) approaches consistently improve word- and character-level accuracy over baseline outputs. The proposed AEC model, combining IPA and alignment features, reduced the average WER of ASR models from 51.56 to 39.82 before augmentation (and 51.56 to 43.59 after augmentation) and improving chrF++ scores from 0.5864 to 0.627, demonstrating consistent gains over the baseline ASR outputs without AEC. Our results highlight the robustness of AEC and the importance of feature design for improving ASR outputs in low-resource settings.
Citation
2025 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing Isai Nlp 2025, 2025