# LSTM-LM with Long-Term History for First-Pass Decoding in Conversational Speech Recognition

arXiv: https://arxiv.org/abs/2010.11349 · HTML: https://mlsanity.com/paper/2010.11349/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

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

- [Updated Corpora and Benchmarks for Long-Form Speech Recognition](https://mlsanity.com/paper/2309.15013/) (2023)
- [Towards Effective and Compact Contextual Representation for Conformer Transducer Speech Recognition Systems](https://mlsanity.com/paper/2306.13307/) (2023)
- [Exploring SSL Discrete Speech Features for Zipformer-based Contextual ASR](https://mlsanity.com/paper/2409.08797/) (2024)
- [Leveraging Cross-Utterance Context For ASR Decoding](https://mlsanity.com/paper/2306.16903/) (2023)

## Abstract (arXiv)

LSTM language models (LSTM-LMs) have been proven to be powerful and yielded significant performance improvements over count based n-gram LMs in modern speech recognition systems. Due to its infinite history states and computational load, most previous studies focus on applying LSTM-LMs in the second-pass for rescoring purpose. Recent work shows that it is feasible and computationally affordable to adopt the LSTM-LMs in the first-pass decoding within a dynamic (or tree based) decoder framework. In this work, the LSTM-LM is composed with a WFST decoder on-the-fly for the first-pass decoding. Furthermore, motivated by the long-term history nature of LSTM-LMs, the use of context beyond the current utterance is explored for the first-pass decoding in conversational speech recognition. The context information is captured by the hidden states of LSTM-LMs across utterance and can be used to guide the first-pass search effectively. The experimental results in our internal meeting transcription system show that significant performance improvements can be obtained by incorporating the contextual information with LSTM-LMs in the first-pass decoding, compared to applying the contextual information in the second-pass rescoring.