# Class LM and word mapping for contextual biasing in End-to-End ASR

arXiv: https://arxiv.org/abs/2007.05609 · HTML: https://mlsanity.com/paper/2007.05609/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 30 of 42)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [Context-Aware Transformer Transducer for Speech Recognition](https://mlsanity.com/paper/2111.03250/) (2021)
- [Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition](https://mlsanity.com/paper/2109.00627/) (2021)
- [Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network](https://mlsanity.com/paper/2305.12493/) (2023)
- [Personalization Strategies for End-to-End Speech Recognition Systems](https://mlsanity.com/paper/2102.07739/) (2021)
- [Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search](https://mlsanity.com/paper/2401.10449/) (2024)
- [CTC-Assisted LLM-Based Contextual ASR](https://mlsanity.com/paper/2411.06437/) (2024)
- [Tree-constrained Pointer Generator with Graph Neural Network Encodings for Contextual Speech Recognition](https://mlsanity.com/paper/2207.00857/) (2022)
- [Improving Contextual Recognition of Rare Words with an Alternate Spelling Prediction Model](https://mlsanity.com/paper/2209.01250/) (2022)
- [Improving ASR Contextual Biasing with Guided Attention](https://mlsanity.com/paper/2401.08835/) (2024)
- [Adaptive Contextual Biasing for Transducer Based Streaming Speech Recognition](https://mlsanity.com/paper/2306.00804/) (2023)
- [Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator](https://mlsanity.com/paper/2205.09058/) (2022)
- [Improving Neural Biasing for Contextual Speech Recognition by Early Context Injection and Text Perturbation](https://mlsanity.com/paper/2407.10303/) (2024)
- [Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Loss](https://mlsanity.com/paper/2406.16120/) (2024)
- [Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm](https://mlsanity.com/paper/2310.00178/) (2023)
- [Contextualized Automatic Speech Recognition with Dynamic Vocabulary](https://mlsanity.com/paper/2405.13344/) (2024)
- [End-to-end contextual asr based on posterior distribution adaptation for hybrid ctc/attention system](https://mlsanity.com/paper/2202.09003/) (2022)
- [Neural-FST Class Language Model for End-to-End Speech Recognition](https://mlsanity.com/paper/2201.11867/) (2022)
- [End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator](https://mlsanity.com/paper/2210.16554/) (2022)
- [Graph Neural Networks for Contextual ASR with the Tree-Constrained Pointer Generator](https://mlsanity.com/paper/2305.18824/) (2023)
- [Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization](https://mlsanity.com/paper/2310.09988/) (2023)
- [Retraining-free Customized ASR for Enharmonic Words Based on a Named-Entity-Aware Model and Phoneme Similarity Estimation](https://mlsanity.com/paper/2305.17846/) (2023)
- [InterBiasing: Boost Unseen Word Recognition through Biasing Intermediate Predictions](https://mlsanity.com/paper/2406.14890/) (2024)
- [Named Entity Detection and Injection for Direct Speech Translation](https://mlsanity.com/paper/2210.11981/) (2022)
- [Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data](https://mlsanity.com/paper/2307.01764/) (2023)
- [Locality enhanced dynamic biasing and sampling strategies for contextual ASR](https://mlsanity.com/paper/2401.13146/) (2024)
- [WCTC-Biasing: Retraining-free Contextual Biasing ASR with Wildcard CTC-based Keyword Spotting and Inter-layer Biasing](https://mlsanity.com/paper/2506.01263/) (2025)
- [Contextual Density Ratio for Language Model Biasing of Sequence to Sequence ASR Systems](https://mlsanity.com/paper/2206.14623/) (2022)
- [SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models](https://mlsanity.com/paper/2507.19361/) (2025)
- [SAN: a robust end-to-end ASR model architecture](https://mlsanity.com/paper/2210.15285/) (2022)

## Abstract (arXiv)

In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid interest in the speech recognition community. They convert speech input to text units in a single trainable Neural Network model. In ASR, many utterances contain rich named entities. Such named entities may be user or location specific and they are not seen during training. A single model makes it inflexible to utilize dynamic contextual information during inference. In this paper, we propose to train a context aware E2E model and allow the beam search to traverse into the context FST during inference. We also propose a simple method to adjust the cost discrepancy between the context FST and the base model. This algorithm is able to reduce the named entity utterance WER by 57% with little accuracy degradation on regular utterances. Although an E2E model does not need pronunciation dictionary, it's interesting to make use of existing pronunciation knowledge to improve accuracy. In this paper, we propose an algorithm to map the rare entity words to common words via pronunciation and treat the mapped words as an alternative form to the original word during recognition. This algorithm further reduces the WER on the named entity utterances by another 31%.