# Implementing contextual biasing in GPU decoder for online ASR

arXiv: https://arxiv.org/abs/2306.15685 · HTML: https://mlsanity.com/paper/2306.15685/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 23 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [Earnings-21: A Practical Benchmark for ASR in the Wild](https://mlsanity.com/paper/2104.11348/) (2021)
- [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://mlsanity.com/paper/2203.16822/) (2022)
- [End-to-end contextual speech recognition using class language models and a token passing decoder](https://mlsanity.com/paper/1812.02142/) (2018)
- [ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications](https://mlsanity.com/paper/2211.04054/) (2022)
- [Improving Contextual Recognition of Rare Words with an Alternate Spelling Prediction Model](https://mlsanity.com/paper/2209.01250/) (2022)
- [Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems](https://mlsanity.com/paper/2104.03643/) (2021)
- [Improving callsign recognition with air-surveillance data in air-traffic communication](https://mlsanity.com/paper/2108.12156/) (2021)
- [A Parallelizable Lattice Rescoring Strategy with Neural Language Models](https://mlsanity.com/paper/2103.05081/) (2021)
- [A two-step approach to leverage contextual data: speech recognition in air-traffic communications](https://mlsanity.com/paper/2202.03725/) (2022)
- [Composing Finite State Transducers on GPUs](https://mlsanity.com/paper/1805.06383/) (2018)
- [GPU-Accelerated Viterbi Exact Lattice Decoder for Batched Online and Offline Speech Recognition](https://mlsanity.com/paper/1910.10032/) (2019)
- [A GPU-based WFST Decoder with Exact Lattice Generation](https://mlsanity.com/paper/1804.03243/) (2018)
- [Lessons Learned in ATCO2: 5000 hours of Air Traffic Control Communications for Robust Automatic Speech Recognition and Understanding](https://mlsanity.com/paper/2305.01155/) (2023)

## Built on by (most cited first, 4 of 4)

- [Unifying Global and Near-Context Biasing in a Single Trie Pass](https://mlsanity.com/paper/2409.13514/) (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)
- [Efficient Trie-based Biasing using K-step Prediction for Rare Word Recognition](https://mlsanity.com/paper/2509.09196/) (2025)
- [Massive Open-Vocabulary Keyword Spotting](https://mlsanity.com/paper/2606.11279/) (2026)

## Abstract (arXiv)

GPU decoding significantly accelerates the output of ASR predictions. While GPUs are already being used for online ASR decoding, post-processing and rescoring on GPUs have not been properly investigated yet. Rescoring with available contextual information can considerably improve ASR predictions. Previous studies have proven the viability of lattice rescoring in decoding and biasing language model (LM) weights in offline and online CPU scenarios. In real-time GPU decoding, partial recognition hypotheses are produced without lattice generation, which makes the implementation of biasing more complex. The paper proposes and describes an approach to integrate contextual biasing in real-time GPU decoding while exploiting the standard Kaldi GPU decoder. Besides the biasing of partial ASR predictions, our approach also permits dynamic context switching allowing a flexible rescoring per each speech segment directly on GPU. The code is publicly released and tested with open-sourced test sets.