# Internal Language Model Estimation for Domain-Adaptive End-to-End Speech Recognition

arXiv: https://arxiv.org/abs/2011.01991 · HTML: https://mlsanity.com/paper/2011.01991/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 30 of 80)

- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion](https://mlsanity.com/paper/2104.02194/) (2021)
- [E-Branchformer: Branchformer with Enhanced merging for speech recognition](https://mlsanity.com/paper/2210.00077/) (2022)
- [LLM Internal States Reveal Hallucination Risk Faced With a Query](https://mlsanity.com/paper/2407.03282/) (2024)
- [Can Generative Large Language Models Perform ASR Error Correction?](https://mlsanity.com/paper/2307.04172/) (2023)
- [Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition](https://mlsanity.com/paper/2306.16007/) (2023)
- [Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding](https://mlsanity.com/paper/2104.02138/) (2021)
- [Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition](https://mlsanity.com/paper/2102.01380/) (2021)
- [SynthASR: Unlocking Synthetic Data for Speech Recognition](https://mlsanity.com/paper/2106.07803/) (2021)
- [Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition](https://mlsanity.com/paper/2109.00627/) (2021)
- [Factorized Neural Transducer for Efficient Language Model Adaptation](https://mlsanity.com/paper/2110.01500/) (2021)
- [Librispeech Transducer Model with Internal Language Model Prior Correction](https://mlsanity.com/paper/2104.03006/) (2021)
- [Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models](https://mlsanity.com/paper/2104.05544/) (2021)
- [ASR Error Correction using Large Language Models](https://mlsanity.com/paper/2409.09554/) (2024)
- [Can Contextual Biasing Remain Effective with Whisper and GPT-2?](https://mlsanity.com/paper/2306.01942/) (2023)
- [Integrating Text Inputs For Training and Adapting RNN Transducer ASR Models](https://mlsanity.com/paper/2202.13155/) (2022)
- [Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network](https://mlsanity.com/paper/2104.11127/) (2021)
- [Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition](https://mlsanity.com/paper/2106.02302/) (2021)
- [Internal Language Model Adaptation with Text-Only Data for End-to-End Speech Recognition](https://mlsanity.com/paper/2110.05354/) (2021)
- [On Language Model Integration for RNN Transducer based Speech Recognition](https://mlsanity.com/paper/2110.06841/) (2021)
- [Modular Hybrid Autoregressive Transducer](https://mlsanity.com/paper/2210.17049/) (2022)
- [Efficient Training of Neural Transducer for Speech Recognition](https://mlsanity.com/paper/2204.10586/) (2022)
- [Improving Rare Word Recognition with LM-aware MWER Training](https://mlsanity.com/paper/2204.07553/) (2022)
- [Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator](https://mlsanity.com/paper/2205.09058/) (2022)
- [Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models](https://mlsanity.com/paper/2212.01992/) (2022)
- [Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation](https://mlsanity.com/paper/2301.13003/) (2023)
- [CB-Conformer: Contextual biasing Conformer for biased word recognition](https://mlsanity.com/paper/2304.09607/) (2023)
- [Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition](https://mlsanity.com/paper/2409.09785/) (2024)
- [Internal Language Model Estimation Through Explicit Context Vector Learning for Attention-based Encoder-decoder ASR](https://mlsanity.com/paper/2201.11627/) (2022)

## Abstract (arXiv)

The external language models (LM) integration remains a challenging task for end-to-end (E2E) automatic speech recognition (ASR) which has no clear division between acoustic and language models. In this work, we propose an internal LM estimation (ILME) method to facilitate a more effective integration of the external LM with all pre-existing E2E models with no additional model training, including the most popular recurrent neural network transducer (RNN-T) and attention-based encoder-decoder (AED) models. Trained with audio-transcript pairs, an E2E model implicitly learns an internal LM that characterizes the training data in the source domain. With ILME, the internal LM scores of an E2E model are estimated and subtracted from the log-linear interpolation between the scores of the E2E model and the external LM. The internal LM scores are approximated as the output of an E2E model when eliminating its acoustic components. ILME can alleviate the domain mismatch between training and testing, or improve the multi-domain E2E ASR. Experimented with 30K-hour trained RNN-T and AED models, ILME achieves up to 15.5% and 6.8% relative word error rate reductions from Shallow Fusion on out-of-domain LibriSpeech and in-domain Microsoft production test sets, respectively.