# Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1810.13025 · HTML: https://mlsanity.com/paper/1810.13025/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 12 of 12)

- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [Learning Word-Level Confidence For Subword End-to-End ASR](https://mlsanity.com/paper/2103.06716/) (2021)
- [XTREME-S: Evaluating Cross-lingual Speech Representations](https://mlsanity.com/paper/2203.10752/) (2022)
- [ASR Rescoring and Confidence Estimation with ELECTRA](https://mlsanity.com/paper/2110.01857/) (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)
- [Word-level confidence estimation for RNN transducers](https://mlsanity.com/paper/2110.15222/) (2021)
- [Who Needs Decoders? Efficient Estimation of Sequence-level Attributes](https://mlsanity.com/paper/2305.05098/) (2023)
- [Adopting Whisper for Confidence Estimation](https://mlsanity.com/paper/2502.13446/) (2025)
- [Accurate and Reliable Confidence Estimation Based on Non-Autoregressive End-to-End Speech Recognition System](https://mlsanity.com/paper/2305.10680/) (2023)

## Abstract (arXiv)

The standard approach to assess reliability of automatic speech transcriptions is through the use of confidence scores. If accurate, these scores provide a flexible mechanism to flag transcription errors for upstream and downstream applications. One challenging type of errors that recognisers make are deletions. These errors are not accounted for by the standard confidence estimation schemes and are hard to rectify in the upstream and downstream processing. High deletion rates are prominent in limited resource and highly mismatched training/testing conditions studied under IARPA Babel and Material programs. This paper looks at the use of bidirectional recurrent neural networks to yield confidence estimates in predicted as well as deleted words. Several simple schemes are examined for combination. To assess usefulness of this approach, the combined confidence score is examined for untranscribed data selection that favours transcriptions with lower deletion errors. Experiments are conducted using IARPA Babel/Material program languages.