mlsanityData to 7 Oct 2026

Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation

arXiv 2312.03312 ↗First posted 6 Dec 2023cs.CL cs.SD eess.AScited by 5

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Abstract (arXiv)

This research optimizes two-pass cross-lingual transfer learning in low-resource languages by enhancing phoneme recognition and phoneme-to-grapheme translation models. Our approach optimizes these two stages to improve speech recognition across languages. We optimize phoneme vocabulary coverage by merging phonemes based on shared articulatory characteristics, thus improving recognition accuracy. Additionally, we introduce a global phoneme noise generator for realistic ASR noise during phoneme-to-grapheme training to reduce error propagation. Experiments on the CommonVoice 12.0 dataset show significant reductions in Word Error Rate (WER) for low-resource languages, highlighting the effectiveness of our approach. This research contributes to the advancements of two-pass ASR systems in low-resource languages, offering the potential for improved cross-lingual transfer learning.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

14 of 23 references.

References · 14

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
  4. 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 496
  5. 2017Multilingual Speech Recognition With A Single End-To-End Modelcited by 87
  6. 2020Universal Phone Recognition with a Multilingual Allophone Systemcited by 81
  7. 2021Simple and Effective Zero-shot Cross-lingual Phoneme Recognitioncited by 79
  8. 2021UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Datacited by 76
  9. 2019A spelling correction model for end-to-end speech recognitioncited by 63
  10. 2020Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capabilitycited by 63
  11. 2019Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Modelcited by 52
  12. 2021Joint Unsupervised and Supervised Training for Multilingual ASRcited by 39
2 more
  1. 2021Pseudo-Labeling for Massively Multilingual Speech Recognitioncited by 23
  2. 2023TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognitioncited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025LLM-based phoneme-to-grapheme for phoneme-based speech recognitioncited by 3
  2. 2025Pronunciation-Lexicon Free Training for Phoneme-based Crosslingual ASR via Joint Stochastic Approximationcited by 3
  3. 2026Advancing LLM-based phoneme-to-grapheme for multilingual speech recognitioncited by 0
  4. 2026Curriculum-Based Noise Adaptation for Phoneme-to-Text Reconstruction in Visual Speech Recognitioncited by 0
  5. 2026Phonologically Informed Tokenization for German Speech Recognition: A Cross-Domain Studycited by 0