mlsanityData to 7 Oct 2026

Automatic Text Pronunciation Correlation Generation and Application for Contextual Biasing

arXiv 2501.00804 ↗First posted 1 Jan 2025eess.AS cs.CLcited by 0

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Abstract (arXiv)

Effectively distinguishing the pronunciation correlations between different written texts is a significant issue in linguistic acoustics. Traditionally, such pronunciation correlations are obtained through manually designed pronunciation lexicons. In this paper, we propose a data-driven method to automatically acquire these pronunciation correlations, called automatic text pronunciation correlation (ATPC). The supervision required for this method is consistent with the supervision needed for training end-to-end automatic speech recognition (E2E-ASR) systems, i.e., speech and corresponding text annotations. First, the iteratively-trained timestamp estimator (ITSE) algorithm is employed to align the speech with their corresponding annotated text symbols. Then, a speech encoder is used to convert the speech into speech embeddings. Finally, we compare the speech embeddings distances of different text symbols to obtain ATPC. Experimental results on Mandarin show that ATPC enhances E2E-ASR performance in contextual biasing and holds promise for dialects or languages lacking artificial pronunciation lexicons.

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References · 11

  1. 2015Listen, Attend and Spellcited by 542
  2. 2020Unsupervised Cross-lingual Representation Learning for Speech Recognitioncited by 496
  3. 2021Layer-wise Analysis of a Self-supervised Speech Representation Modelcited by 293
  4. 2021Recent Advances in End-to-End Automatic Speech Recognitioncited by 138
  5. 2022WeNet 2.0: More Productive End-to-End Speech Recognition Toolkitcited by 46
  6. 2023Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Networkcited by 31
  7. 2020Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducercited by 22
  8. 2020CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognitioncited by 20
  9. 2022Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systemscited by 17
  10. 2020Modular End-to-end Automatic Speech Recognition Framework for Acoustic-to-word Modelcited by 4
  11. 2023Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecturecited by 1

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