Covid-19 driven pandemic situation has brought greater visibility to contactless interactions with consumer IoT devices. As industries explore transitioning away from shared touch devices, the role of voice biometrics for authentication becomes critical. Voice biometrics is utilized for voice recognition through analysis of an individual’s pitch, speech, voice, and tone and has been used in back-office operations for customer verification, fraud avoidance, and password reset. However, not much research has been done in the consumer sector and the critical role of trust in driving usage and the adoption of such services.
Using the existing research on trust in e-commerce and automation, we bring together models from psychology (Theory of Planned Behaviour) and technology (Human-Centered Artificial Intelligence), to explore the various antecedents of consumer trust for voice authentication (Ease of use, self-efficacy, perceived usefulness, reliability, the perceived reputation of the service provider, perceived security, perceived privacy, fraud, and social influence). Special attention is given to the use of vernacular voice, two-step authentication, and their impact on trust. Speaker recognition is a pattern recognition problem and incudes various technologies like frequency estimation, hidden Markov models, Gaussian mixture models, pattern matching algorithms, neural networks, decision trees, and linear predictive coding.
Through a combination of custom build prototypes, usage scenarios, and qualitative and quantitative analysis, we intend to highlight the components that drive trust for voice authentication so that it can help in the societal transition to contactless interactions. Early results show that people value Security, Privacy, and Reliability as top factors impacting trust in Voice Biometrics. Multi-level authentication, vernacular voice, and initial usage for transactional and low-value financial transactions can help drive trust in the voice biometrics ecosystem.
