Smart and Robust Speaker Recognition for Context-Aware In-Vehicle Applications

Abstract

The importance of robust audio speech processing has rapidly increased in the latest years, as the number of smart and connected devices is growing. This effect is strongly related to the Internet of things framework, introducing concepts such as connected vehicles and future smart cities. Context-aware applications are fundamental in this evolving environment, enabling smart and custom-tailored services for a variety of users. The use of on-board speaker recognition (SR) systems can play a key role in enhancing the customization of in-vehicle applications, by identifying the actual users and personalizing services based on their identity. Driven by this motivation, in this paper we present a performance study of an SR system, designed to face typical challenging conditions of an in-vehicle environment. We propose the design of a robust speaker identification algorithm embedding a smart preprocessing method based on voice activity detection, which can effectively reduce the influence of noise and distance on classification. Results show that our solution is able to efficiently improve the correct classification rate, even in the case of distant audio acquisition and in a variety of noisy environments.