# Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization

arXiv: https://arxiv.org/abs/2310.09988 · HTML: https://mlsanity.com/paper/2310.09988/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 15 of 15)

- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages](https://mlsanity.com/paper/2303.01037/) (2023)
- [Deep context: end-to-end contextual speech recognition](https://mlsanity.com/paper/1808.02480/) (2018)
- [WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2102.01547/) (2021)
- [Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition](https://mlsanity.com/paper/2011.01991/) (2020)
- [EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding](https://mlsanity.com/paper/1507.08240/) (2015)
- [A Density Ratio Approach to Language Model Fusion in End-To-End Automatic Speech Recognition](https://mlsanity.com/paper/2002.11268/) (2020)
- [Class LM and word mapping for contextual biasing in End-to-End ASR](https://mlsanity.com/paper/2007.05609/) (2020)
- [SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognition](https://mlsanity.com/paper/1910.01992/) (2019)
- [G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR](https://mlsanity.com/paper/1910.12612/) (2019)
- [Acoustic Model Fusion for End-to-end Speech Recognition](https://mlsanity.com/paper/2310.07062/) (2023)

## Built on by (most cited first, 6 of 6)

- [Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation](https://mlsanity.com/paper/2407.10303/) (2024)
- [Contextualization of ASR with LLM using phonetic retrieval-based augmentation](https://mlsanity.com/paper/2409.15353/) (2024)
- [New Solutions on LLM Acceleration, Optimization, and Application](https://mlsanity.com/paper/2406.10903/) (2024)
- [Conformer-Based Speech Recognition On Extreme Edge-Computing Devices](https://mlsanity.com/paper/2312.10359/) (2023)
- [Enhancing CTC-based speech recognition with diverse modeling units](https://mlsanity.com/paper/2406.03274/) (2024)
- [Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modeling](https://mlsanity.com/paper/2509.05908/) (2025)

## Abstract (arXiv)

Recent advances in deep learning and automatic speech recognition have improved the accuracy of end-to-end speech recognition systems, but recognition of personal content such as contact names remains a challenge. In this work, we describe our personalization solution for an end-to-end speech recognition system based on connectionist temporal classification. Building on previous work, we present a novel method for generating additional subword tokenizations for personal entities from their pronunciations. We show that using this technique in combination with two established techniques, contextual biasing and wordpiece prior normalization, we are able to achieve personal named entity accuracy on par with a competitive hybrid system.