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Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks

Abstract: 

Recently, there has been growth in providers of speech transcription services enabling others to leverage technology they would not normally be able to use. As a result, speech-enabled solutions have become commonplace. Their success critically relies on the quality, accuracy, and reliability of the underlying speech transcription systems. Those black box systems, however, offer limited means for quality control as only word sequences are typically available. This paper examines this limited resource scenario for confidence estimation, a measure commonly used to assess transcription reliability. In particular, it explores what other sources of word and sub-word level information available in the transcription process could be used to improve confidence scores. To encode all such information this paper extends lattice recurrent neural networks to handle sub-words. Experimental results using the IARPA OpenKWS 2016 evaluation system show that the use of additional information yields significant gains in confidence estimation accuracy. The implementation for this model can be found online.

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Paper Details

Authors:
A. Kastanos, A. Ragni, M.J.F. Gales
Submitted On:
14 May 2020 - 4:29pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Alexandros Kastanos
Paper Code:
2710
Document Year:
2020
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Document Files

Black-Box-ASR-ICASSP-2020.pdf

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[1] A. Kastanos, A. Ragni, M.J.F. Gales, "Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5320. Accessed: Sep. 26, 2020.
@article{5320-20,
url = {http://sigport.org/5320},
author = {A. Kastanos; A. Ragni; M.J.F. Gales },
publisher = {IEEE SigPort},
title = {Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks},
year = {2020} }
TY - EJOUR
T1 - Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks
AU - A. Kastanos; A. Ragni; M.J.F. Gales
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5320
ER -
A. Kastanos, A. Ragni, M.J.F. Gales. (2020). Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks. IEEE SigPort. http://sigport.org/5320
A. Kastanos, A. Ragni, M.J.F. Gales, 2020. Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks. Available at: http://sigport.org/5320.
A. Kastanos, A. Ragni, M.J.F. Gales. (2020). "Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks." Web.
1. A. Kastanos, A. Ragni, M.J.F. Gales. Confidence Estimation for Black Box Automatic Speech Recognition Systems Using Lattice Recurrent Neural Networks [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5320