ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2101.05525
  4. Cited By
An evaluation of word-level confidence estimation for end-to-end
  automatic speech recognition

An evaluation of word-level confidence estimation for end-to-end automatic speech recognition

14 January 2021
Dan Oneaţă
Alexandru Caranica
Adriana Stan
H. Cucu
    UQCV
ArXivPDFHTML

Papers citing "An evaluation of word-level confidence estimation for end-to-end automatic speech recognition"

8 / 8 papers shown
Title
Hystoc: Obtaining word confidences for fusion of end-to-end ASR systems
Hystoc: Obtaining word confidences for fusion of end-to-end ASR systems
Karel Beneš
M. Kocour
L. Burget
40
2
0
21 May 2023
How Does Beam Search improve Span-Level Confidence Estimation in
  Generative Sequence Labeling?
How Does Beam Search improve Span-Level Confidence Estimation in Generative Sequence Labeling?
Kazuma Hashimoto
Iftekhar Naim
K. Raman
UQLM
31
2
0
21 Dec 2022
Fast Entropy-Based Methods of Word-Level Confidence Estimation for
  End-To-End Automatic Speech Recognition
Fast Entropy-Based Methods of Word-Level Confidence Estimation for End-To-End Automatic Speech Recognition
A. Laptev
Boris Ginsburg
46
7
0
16 Dec 2022
Cross-Modal ASR Post-Processing System for Error Correction and
  Utterance Rejection
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection
Jing Du
Shiliang Pu
Qinbo Dong
Chao Jin
Xin Qi
Dian Gu
Ru Wu
Hongwei Zhou
39
9
0
10 Jan 2022
Recent Advances in End-to-End Automatic Speech Recognition
Recent Advances in End-to-End Automatic Speech Recognition
Jinyu Li
VLM
40
363
0
02 Nov 2021
Residual Energy-Based Models for End-to-End Speech Recognition
Residual Energy-Based Models for End-to-End Speech Recognition
Qiujia Li
Yu Zhang
Bo-wen Li
Liangliang Cao
P. Woodland
31
14
0
25 Mar 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,695
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,167
0
06 Jun 2015
1