  Speech-centric machine learning systems have revolutionized many leading
domains ranging from transportation and healthcare to education and defense,
profoundly changing how people live, work, and interact with each other.
However, recent studies have demonstrated that many speech-centric ML systems
may need to be considered more trustworthy for broader deployment.
Specifically, concerns over privacy breaches, discriminating performance, and
vulnerability to adversarial attacks have all been discovered in ML research
fields. In order to address the above challenges and risks, a significant
number of efforts have been made to ensure these ML systems are trustworthy,
especially private, safe, and fair. In this paper, we conduct the first
comprehensive survey on speech-centric trustworthy ML topics related to
privacy, safety, and fairness. In addition to serving as a summary report for
the research community, we point out several promising future research
directions to inspire the researchers who wish to explore further in this area.
