# Bi-Directional Lattice Recurrent Neural Networks for Confidence Estimation

arXiv: https://arxiv.org/abs/1810.13024 · HTML: https://mlsanity.com/paper/1810.13024/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 11 of 11)

- [Learning Word-Level Confidence For Subword End-to-End ASR](https://mlsanity.com/paper/2103.06716/) (2021)
- [An evaluation of word-level confidence estimation for end-to-end automatic speech recognition](https://mlsanity.com/paper/2101.05525/) (2021)
- [Residual Energy-Based Models for End-to-End Speech Recognition](https://mlsanity.com/paper/2103.14152/) (2021)
- [Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction](https://mlsanity.com/paper/2104.12870/) (2021)
- [Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models](https://mlsanity.com/paper/2405.14161/) (2024)
- [Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition](https://mlsanity.com/paper/2110.03327/) (2021)
- [Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection](https://mlsanity.com/paper/2201.03313/) (2022)
- [Evaluating ASR Confidence Scores for Automated Error Detection in User-Assisted Correction Interfaces](https://mlsanity.com/paper/2503.15124/) (2025)
- [Word-level confidence estimation for RNN transducers](https://mlsanity.com/paper/2110.15222/) (2021)
- [Late multimodal fusion for image and audio music transcription](https://mlsanity.com/paper/2204.03063/) (2022)
- [TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASR](https://mlsanity.com/paper/2401.03251/) (2024)

## Abstract (arXiv)

The standard approach to mitigate errors made by an automatic speech recognition system is to use confidence scores associated with each predicted word. In the simplest case, these scores are word posterior probabilities whilst more complex schemes utilise bi-directional recurrent neural network (BiRNN) models. A number of upstream and downstream applications, however, rely on confidence scores assigned not only to 1-best hypotheses but to all words found in confusion networks or lattices. These include but are not limited to speaker adaptation, semi-supervised training and information retrieval. Although word posteriors could be used in those applications as confidence scores, they are known to have reliability issues. To make improved confidence scores more generally available, this paper shows how BiRNNs can be extended from 1-best sequences to confusion network and lattice structures. Experiments are conducted using one of the Cambridge University submissions to the IARPA OpenKWS 2016 competition. The results show that confusion network and lattice-based BiRNNs can provide a significant improvement in confidence estimation.