# CB-Conformer: Contextual biasing Conformer for biased word recognition

arXiv: https://arxiv.org/abs/2304.09607 · HTML: https://mlsanity.com/paper/2304.09607/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- Builds on 21 of its 25 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Streaming End-to-end Speech Recognition For Mobile Devices](https://mlsanity.com/paper/1811.06621/) (2018)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://mlsanity.com/paper/2110.03370/) (2021)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2102.01547/) (2021)
- [An analysis of incorporating an external language model into a sequence-to-sequence model](https://mlsanity.com/paper/1712.01996/) (2017)
- [Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition](https://mlsanity.com/paper/2011.01991/) (2020)
- [Deep Shallow Fusion for RNN-T Personalization](https://mlsanity.com/paper/2011.07754/) (2020)
- [Contextual Adapters for Personalized Speech Recognition in Neural Transducers](https://mlsanity.com/paper/2205.13660/) (2022)
- [Towards Fast and Accurate Streaming End-to-End ASR](https://mlsanity.com/paper/2004.11544/) (2020)
- [Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities](https://mlsanity.com/paper/1912.09251/) (2019)
- [Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection](https://mlsanity.com/paper/2201.12806/) (2022)
- [Unsupervised Domain Adaptation for Robust Speech Recognition via Variational Autoencoder-Based Data Augmentation](https://mlsanity.com/paper/1707.06265/) (2017)
- [Personalization Strategies for End-to-End Speech Recognition Systems](https://mlsanity.com/paper/2102.07739/) (2021)
- [Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models](https://mlsanity.com/paper/2104.05544/) (2021)
- [Fast Contextual Adaptation with Neural Associative Memory for On-Device Personalized Speech Recognition](https://mlsanity.com/paper/2110.02220/) (2021)
- [Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network](https://mlsanity.com/paper/2104.11127/) (2021)
- [On Language Model Integration for RNN Transducer based Speech Recognition](https://mlsanity.com/paper/2110.06841/) (2021)
- [ProtoSound: A Personalized and Scalable Sound Recognition System for Deaf and Hard-of-Hearing Users](https://mlsanity.com/paper/2202.11134/) (2022)
- [Domain Adaptation of low-resource Target-Domain models using well-trained ASR Conformer Models](https://mlsanity.com/paper/2202.09167/) (2022)

## Built on by (most cited first, 11 of 11)

- [Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning](https://mlsanity.com/paper/2307.10274/) (2023)
- [Improving Domain-Specific ASR with LLM-Generated Contextual Descriptions](https://mlsanity.com/paper/2407.17874/) (2024)
- [A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting](https://mlsanity.com/paper/2309.09552/) (2023)
- [Deferred NAM: Low-latency Top-K Context Injection via Deferred Context Encoding for Non-Streaming ASR](https://mlsanity.com/paper/2404.10180/) (2024)
- [Keyword-Guided Adaptation of Automatic Speech Recognition](https://mlsanity.com/paper/2406.02649/) (2024)
- [Locality enhanced dynamic biasing and sampling strategies for contextual ASR](https://mlsanity.com/paper/2401.13146/) (2024)
- [An efficient text augmentation approach for contextualized Mandarin speech recognition](https://mlsanity.com/paper/2406.09950/) (2024)
- [An Effective Context-Balanced Adaptation Approach for Long-Tailed Speech Recognition](https://mlsanity.com/paper/2409.06468/) (2024)
- [Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation](https://mlsanity.com/paper/2505.23077/) (2025)
- [Enhancing the Robustness of Contextual ASR to Varying Biasing Information Volumes Through Purified Semantic Correlation Joint Modeling](https://mlsanity.com/paper/2509.05908/) (2025)
- [LSZone: A Lightweight Spatial Information Modeling Architecture for Real-time In-car Multi-zone Speech Separation](https://mlsanity.com/paper/2510.10687/) (2025)

## Abstract (arXiv)

Due to the mismatch between the source and target domains, how to better utilize the biased word information to improve the performance of the automatic speech recognition model in the target domain becomes a hot research topic. Previous approaches either decode with a fixed external language model or introduce a sizeable biasing module, which leads to poor adaptability and slow inference. In this work, we propose CB-Conformer to improve biased word recognition by introducing the Contextual Biasing Module and the Self-Adaptive Language Model to vanilla Conformer. The Contextual Biasing Module combines audio fragments and contextual information, with only 0.2% model parameters of the original Conformer. The Self-Adaptive Language Model modifies the internal weights of biased words based on their recall and precision, resulting in a greater focus on biased words and more successful integration with the automatic speech recognition model than the standard fixed language model. In addition, we construct and release an open-source Mandarin biased-word dataset based on WenetSpeech. Experiments indicate that our proposed method brings a 15.34% character error rate reduction, a 14.13% biased word recall increase, and a 6.80% biased word F1-score increase compared with the base Conformer.