# Automatic Text Pronunciation Correlation Generation and Application for Contextual Biasing

arXiv: https://arxiv.org/abs/2501.00804 · HTML: https://mlsanity.com/paper/2501.00804/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Layer-wise Analysis of a Self-supervised Speech Representation Model](https://mlsanity.com/paper/2107.04734/) (2021)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2203.15455/) (2022)
- [Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network](https://mlsanity.com/paper/2305.12493/) (2023)
- [Improved Neural Language Model Fusion for Streaming Recurrent Neural Network Transducer](https://mlsanity.com/paper/2010.13878/) (2020)
- [CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition](https://mlsanity.com/paper/2012.09466/) (2020)
- [Towards Contextual Spelling Correction for Customization of End-to-end Speech Recognition Systems](https://mlsanity.com/paper/2203.00888/) (2022)
- [Modular End-to-end Automatic Speech Recognition Framework for Acoustic-to-word Model](https://mlsanity.com/paper/2008.00953/) (2020)
- [Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture](https://mlsanity.com/paper/2307.02351/) (2023)

## 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.