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Analyzing Uncertainties in Speech Recognition Using Dropout

Abstract: 

The performance of Automatic Speech Recognition (ASR) systems is often measured using Word Error Rates (WER) which requires time-consuming and expensive manually transcribed data. In this paper, we use state-of-the-art ASR systems based on Deep Neural Networks (DNN) and propose a novel framework which uses ``Dropout'' at the test time to model uncertainty in prediction hypotheses. We systematically exploit this uncertainty to estimate WER without the need for explicit transcriptions. In addition, we show that the predictive uncertainty can also be used to accurately localize the errors made by the ASR system. We study the performance of our approach on Switchboard database where it predicts WER accurately within a range of 2.6% and 5.0% for HMM-DNN and Connectionist Temporal Classification (CTC) ASR systems, respectively.

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Paper Details

Authors:
Hervé Bourlard
Submitted On:
11 May 2019 - 8:55am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Apoorv Vyas
Paper Code:
4576
Document Year:
2019
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Document Files

Poster_avyas_ICASSP_2019.pdf

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[1] Hervé Bourlard , "Analyzing Uncertainties in Speech Recognition Using Dropout", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4441. Accessed: Jun. 19, 2019.
@article{4441-19,
url = {http://sigport.org/4441},
author = { Hervé Bourlard },
publisher = {IEEE SigPort},
title = {Analyzing Uncertainties in Speech Recognition Using Dropout},
year = {2019} }
TY - EJOUR
T1 - Analyzing Uncertainties in Speech Recognition Using Dropout
AU - Hervé Bourlard
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4441
ER -
Hervé Bourlard . (2019). Analyzing Uncertainties in Speech Recognition Using Dropout. IEEE SigPort. http://sigport.org/4441
Hervé Bourlard , 2019. Analyzing Uncertainties in Speech Recognition Using Dropout. Available at: http://sigport.org/4441.
Hervé Bourlard . (2019). "Analyzing Uncertainties in Speech Recognition Using Dropout." Web.
1. Hervé Bourlard . Analyzing Uncertainties in Speech Recognition Using Dropout [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4441