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A Study of Gender Impact in Self-supervised Models for Speech-to-Text
  Systems

A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems

4 April 2022
Marcely Zanon Boito
Laurent Besacier
N. Tomashenko
Yannick Esteve
ArXivPDFHTML

Papers citing "A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems"

4 / 4 papers shown
Title
Some voices are too common: Building fair speech recognition systems
  using the Common Voice dataset
Some voices are too common: Building fair speech recognition systems using the Common Voice dataset
Lucas Maison
Yannick Esteve
21
3
0
01 Jun 2023
How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An
  Extensive Benchmark on Air Traffic Control Communications
How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications
Juan Pablo Zuluaga
Amrutha Prasad
Iuliia Nigmatulina
Seyyed Saeed Sarfjoo
P. Motlícek
Matthias Kleinert
H. Helmke
Oliver Ohneiser
Qingran Zhan
13
43
0
31 Mar 2022
Don't speak too fast: The impact of data bias on self-supervised speech
  models
Don't speak too fast: The impact of data bias on self-supervised speech models
Yen Meng
Yi-Hui Chou
Andy T. Liu
Hung-yi Lee
34
24
0
15 Oct 2021
Model-Based Approach for Measuring the Fairness in ASR
Model-Based Approach for Measuring the Fairness in ASR
Zhe Liu
Irina-Elena Veliche
Fuchun Peng
42
22
0
19 Sep 2021
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