# Accurate and Reliable Confidence Estimation Based on Non-Autoregressive End-to-End Speech Recognition System

arXiv: https://arxiv.org/abs/2305.10680 · HTML: https://mlsanity.com/paper/2305.10680/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning](https://mlsanity.com/paper/1609.06773/) (2016)
- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://mlsanity.com/paper/2206.08317/) (2022)
- [CIF: Continuous Integrate-and-Fire for End-to-End Speech Recognition](https://mlsanity.com/paper/1905.11235/) (2019)
- [Improving RNN Transducer Modeling for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.12415/) (2019)
- [Transformers with convolutional context for ASR](https://mlsanity.com/paper/1904.11660/) (2019)
- [Confidence Estimation for Attention-based Sequence-to-sequence Models for Speech Recognition](https://mlsanity.com/paper/2010.11428/) (2020)
- [Why does CTC result in peaky behavior?](https://mlsanity.com/paper/2105.14849/) (2021)
- [ASR Rescoring and Confidence Estimation with ELECTRA](https://mlsanity.com/paper/2110.01857/) (2021)
- [Confidence Estimation and Deletion Prediction Using Bidirectional Recurrent Neural Networks](https://mlsanity.com/paper/1810.13025/) (2018)
- [Boundary and Context Aware Training for CIF-based Non-Autoregressive End-to-end ASR](https://mlsanity.com/paper/2104.04702/) (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)
- [Improving Confidence Estimation on Out-of-Domain Data for End-to-End Speech Recognition](https://mlsanity.com/paper/2110.03327/) (2021)
- [Confidence Score Based Conformer Speaker Adaptation for Speech Recognition](https://mlsanity.com/paper/2206.12045/) (2022)

## Abstract (arXiv)

Estimating confidence scores for recognition results is a classic task in ASR field and of vital importance for kinds of downstream tasks and training strategies. Previous end-to-end~(E2E) based confidence estimation models (CEM) predict score sequences of equal length with input transcriptions, leading to unreliable estimation when deletion and insertion errors occur. In this paper we proposed CIF-Aligned confidence estimation model (CA-CEM) to achieve accurate and reliable confidence estimation based on novel non-autoregressive E2E ASR model - Paraformer. CA-CEM utilizes the modeling character of continuous integrate-and-fire (CIF) mechanism to generate token-synchronous acoustic embedding, which solves the estimation failure issue above. We measure the quality of estimation with AUC and RMSE in token level and ECE-U - a proposed metrics in utterance level. CA-CEM gains 24% and 19% relative reduction on ECE-U and also better AUC and RMSE on two test sets. Furthermore, we conduct analysis to explore the potential of CEM for different ASR related usage.