# CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition

arXiv: https://arxiv.org/abs/2012.09466 · HTML: https://mlsanity.com/paper/2012.09466/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2020.

## Built on by (most cited first, 21 of 21)

- [X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages](https://mlsanity.com/paper/2305.04160/) (2023)
- [Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection](https://mlsanity.com/paper/2201.12806/) (2022)
- [Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network](https://mlsanity.com/paper/2305.12493/) (2023)
- [CTC-Assisted LLM-Based Contextual ASR](https://mlsanity.com/paper/2411.06437/) (2024)
- [SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability](https://mlsanity.com/paper/2308.03266/) (2023)
- [CopyNE: Better Contextual ASR by Copying Named Entities](https://mlsanity.com/paper/2305.12839/) (2023)
- [Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition](https://mlsanity.com/paper/2306.00804/) (2023)
- [Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation](https://mlsanity.com/paper/2301.13003/) (2023)
- [Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Loss](https://mlsanity.com/paper/2406.16120/) (2024)
- [Improving Large-scale Deep Biasing with Phoneme Features and Text-only Data in Streaming Transducer](https://mlsanity.com/paper/2311.08966/) (2023)
- [Can Visual Context Improve Automatic Speech Recognition for an Embodied Agent?](https://mlsanity.com/paper/2210.13189/) (2022)
- [Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding](https://mlsanity.com/paper/2303.01341/) (2023)
- [An efficient text augmentation approach for contextualized Mandarin speech recognition](https://mlsanity.com/paper/2406.09950/) (2024)
- [VHASR: A Multimodal Speech Recognition System With Vision Hotwords](https://mlsanity.com/paper/2410.00822/) (2024)
- [VILAS: Exploring the Effects of Vision and Language Context in Automatic Speech Recognition](https://mlsanity.com/paper/2305.19972/) (2023)
- [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)
- [Automatic Text Pronunciation Correlation Generation and Application for Contextual Biasing](https://mlsanity.com/paper/2501.00804/) (2025)
- [H-PRM: A Pluggable Hotword Pre-Retrieval Module for Various Speech Recognition Systems](https://mlsanity.com/paper/2508.18295/) (2025)
- [PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation](https://mlsanity.com/paper/2509.04357/) (2025)
- [LLM-HB: Language-Aware LLM-Guided Hotword Biasing for Code-Switching ASR](https://mlsanity.com/paper/2610.16579/) (2026)

## Abstract (arXiv)

End-to-end (E2E) models have achieved promising results on multiple speech recognition benchmarks, and shown the potential to become the mainstream. However, the unified structure and the E2E training hamper injecting contextual information into them for contextual biasing. Though contextual LAS (CLAS) gives an excellent all-neural solution, the degree of biasing to given context information is not explicitly controllable. In this paper, we focus on incorporating context information into the continuous integrate-and-fire (CIF) based model that supports contextual biasing in a more controllable fashion. Specifically, an extra context processing network is introduced to extract contextual embeddings, integrate acoustically relevant context information and decode the contextual output distribution, thus forming a collaborative decoding with the decoder of the CIF-based model. Evaluated on the named entity rich evaluation sets of HKUST/AISHELL-2, our method brings relative character error rate (CER) reduction of 8.83%/21.13% and relative named entity character error rate (NE-CER) reduction of 40.14%/51.50% when compared with a strong baseline. Besides, it keeps the performance on original evaluation set without degradation.