# A Parallelizable Lattice Rescoring Strategy with Neural Language Models

arXiv: https://arxiv.org/abs/2103.05081 · HTML: https://mlsanity.com/paper/2103.05081/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [End-to-end ASR: from Supervised to Semi-Supervised Learning with Modern Architectures](https://mlsanity.com/paper/1911.08460/) (2019)
- [Language Modeling with Deep Transformers](https://mlsanity.com/paper/1905.04226/) (2019)
- [Fully Convolutional Speech Recognition](https://mlsanity.com/paper/1812.06864/) (2018)
- [LSTM Language Models for LVCSR in First-Pass Decoding and Lattice-Rescoring](https://mlsanity.com/paper/1907.01030/) (2019)
- [Lattice Rescoring Strategies for Long Short Term Memory Language Models in Speech Recognition](https://mlsanity.com/paper/1711.05448/) (2017)
- [Future Word Contexts in Neural Network Language Models](https://mlsanity.com/paper/1708.05592/) (2017)
- [Neural Language Modeling With Implicit Cache Pointers](https://mlsanity.com/paper/2009.13774/) (2020)

## Built on by (most cited first, 10 of 10)

- [EURO: ESPnet Unsupervised ASR Open-source Toolkit](https://mlsanity.com/paper/2211.17196/) (2022)
- [Advancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces and Conformers](https://mlsanity.com/paper/2107.03007/) (2021)
- [Private Language Model Adaptation for Speech Recognition](https://mlsanity.com/paper/2110.10026/) (2021)
- [EEL: Efficiently Encoding Lattices for Reranking](https://mlsanity.com/paper/2306.00947/) (2023)
- [Implementing contextual biasing in GPU decoder for online ASR](https://mlsanity.com/paper/2306.15685/) (2023)
- [Adaptive Multi-Corpora Language Model Training for Speech Recognition](https://mlsanity.com/paper/2211.05121/) (2022)
- [Correction Focused Language Model Training for Speech Recognition](https://mlsanity.com/paper/2310.11003/) (2023)
- [LT-LM: a novel non-autoregressive language model for single-shot lattice rescoring](https://mlsanity.com/paper/2104.02526/) (2021)
- [On Sampling-Based Training Criteria for Neural Language Modeling](https://mlsanity.com/paper/2104.10507/) (2021)
- [Self-Normalized Importance Sampling for Neural Language Modeling](https://mlsanity.com/paper/2111.06310/) (2021)

## Abstract (arXiv)

This paper proposes a parallel computation strategy and a posterior-based lattice expansion algorithm for efficient lattice rescoring with neural language models (LMs) for automatic speech recognition. First, lattices from first-pass decoding are expanded by the proposed posterior-based lattice expansion algorithm. Second, each expanded lattice is converted into a minimal list of hypotheses that covers every arc. Each hypothesis is constrained to be the best path for at least one arc it includes. For each lattice, the neural LM scores of the minimal list are computed in parallel and are then integrated back to the lattice in the rescoring stage. Experiments on the Switchboard dataset show that the proposed rescoring strategy obtains comparable recognition performance and generates more compact lattices than a competitive baseline method. Furthermore, the parallel rescoring method offers more flexibility by simplifying the integration of PyTorch-trained neural LMs for lattice rescoring with Kaldi.