mlsanityData to 14 Oct 2026

ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers using Phonetic Features

arXiv 2511.21088 ↗First posted 26 Nov 2025cs.CL cs.LG cs.SDcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 26 Nov 2025.

Builds on

15 of 31 references.

References · 15

  1. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  2. 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 469
  3. 2023Scaling Speech Technology to 1,000+ Languagescited by 411
  4. 2017OpenNMT: Open-source Toolkit for Neural Machine Translationcited by 251
  5. 2019MOSNet: Deep Learning based Objective Assessment for Voice Conversioncited by 127
  6. 2017An analysis of incorporating an external language model into a sequence-to-sequence modelcited by 111
  7. 2023Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Promptingcited by 72
  8. 2023Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognitioncited by 56
  9. 2020ASR Error Correction and Domain Adaptation Using Machine Translationcited by 48
  10. 2020Improving Readability for Automatic Speech Recognition Transcriptioncited by 22
  11. 2024It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognitioncited by 22
  12. 2022Error Correction in ASR using Sequence-to-Sequence Modelscited by 19
3 more
  1. 2024MMGER: Multi-modal and Multi-granularity Generative Error Correction with LLM for Joint Accent and Speech Recognitioncited by 6
  2. 2021Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queriescited by 4
  3. 2024Evolutionary Prompt Design for LLM-Based Post-ASR Error Correctioncited by 4

Built on by

Not cited yet.