Improving Patient Voice Intelligibility by using a Euclidian Distance-Based Approach to Improve Voice Assistant Accuracy

Abstract

Voice assistance (VA) is gaining domestic consumer attention in a variety of products, such as Amazon Alexa, Google Home, Apple’s Siri, and Microsoft’s Cortana. Furthermore, VA has recently shown its usefulness and ability to improve inpatient experience in hospitals and clinics. Nevertheless, none of the VA products has an accuracy rate greater than 90%. The accuracy decreases even more in noisy or public environments. Hence, improving VA accuracy in noisy environments requires a speech signal algorithm with good quality and intelligibility. There is great interest in developing an objective intelligibility measure that shows maximum correlation with subjective speech intelligibility and that can measure the effect of speech enhancement algorithms on the processing of noisy speech signals. In this paper, Euclidian distance-based speech intelligibility prediction is proposed to measure the correlation with subjective intelligibility in different noisy environments. This paper also presents a comparative analysis and general background research in speech intelligibility improvement. The results show that no single algorithm is effective in improving the intelligibility of speech signals.