Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bias in Automated Speaker Recognition   Automated speaker recognition uses data processing to identify speakers by
their voice. Today, automated speaker recognition technologies are deployed on
billions of smart devices and in services such as call centres. Despite their
wide-scale deployment and known sources of bias in face recognition and natural
language processing, bias in automated speaker recognition has not been studied
systematically. We present an in-depth empirical and analytical study of bias
in the machine learning development workflow of speaker verification, a voice
biometric and core task in automated speaker recognition. Drawing on an
established framework for understanding sources of harm in machine learning, we
show that bias exists at every development stage in the well-known VoxCeleb
Speaker Recognition Challenge, including model building, implementation, and
data generation. Most affected are female speakers and non-US nationalities,
who experience significant performance degradation. Leveraging the insights
from our findings, we make practical recommendations for mitigating bias in
automated speaker recognition, and outline future research directions.

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The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn