Scoring Metrics of Assessing Voiceprint Distinctiveness Based on Speech Content and Rate

A voiceprint is the distinctive pattern of human voices widely used for authentication in voice assistants. This paper investigates the impact of speech contents and speech rates on the distinctiveness of voiceprint, and has obtained answers to three questions by studying 2457 speakers and 21,500,000 test samples: 1) What are the influential factors that users can control to affect the distinctiveness of voiceprints? 2) How to quantify the distinctiveness for given speeches, e.g., the speech of wake-up words when activating voice assistants? 3) How to help users select wake-up words and adjust the speech rate to improve distinctiveness levels? To answer those questions, we break down speeches into phones, and experimentally obtain the correlation between false recognition rates and the richness, order, length, and elements of the phones. Then, we define the PROLE Score that can reflect the voice distinctiveness, and evaluate 30 wake-up words of 19 commercial voice assistant products to provide recommendations on selecting secure voiceprint words. We also measure the correlation between false recognition rates and speech rates, and define the TER Score that reveals the distance of distinctiveness from the secure voiceprint, and it guides users to adjust their speech rate to a secure value.