mlsanityData to 14 Oct 2026

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

arXiv 2310.09988 ↗First posted 16 Oct 2023cs.LGcited by 6

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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.

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

15 of 24 references.

References · 15

  1. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
  2. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  3. 2013Speech Recognition with Deep Recurrent Neural Networkscited by 669
  4. 2015Listen, Attend and Spellcited by 543
  5. 2023Scaling Speech Technology to 1,000+ Languagescited by 411
  6. 2023Google USM: Scaling Automatic Speech Recognition Beyond 100 Languagescited by 231
  7. 2018Deep context: end-to-end contextual speech recognitioncited by 137
  8. 2021WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkitcited by 125
  9. 2020Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognitioncited by 80
  10. 2015EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decodingcited by 73
  11. 2020A Density Ratio Approach to Language Model Fusion in End-To-End Automatic Speech Recognitioncited by 73
  12. 2020Class LM and word mapping for contextual biasing in End-to-End ASRcited by 42
3 more
  1. 2019SNDCNN: Self-normalizing deep CNNs with scaled exponential linear units for speech recognitioncited by 13
  2. 2019G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASRcited by 11
  3. 2023Acoustic Model Fusion for End-to-end Speech Recognitioncited by 6

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Cited by year

Citing papers per year: 2023: 1; 2024: 4; 2025: 1.

6 papers.

  1. 2024Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbationcited by 11
  2. 2024Contextualization of ASR with LLM using phonetic retrieval-based augmentationcited by 10
  3. 2024New Solutions on LLM Acceleration, Optimization, and Applicationcited by 7
  4. 2023Conformer-Based Speech Recognition On Extreme Edge-Computing Devicescited by 6
  5. 2024Enhancing CTC-based speech recognition with diverse modeling unitscited by 1
  6. 2025Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modelingcited by 1