A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairness

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