An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications
This is an incremental improvement for in-vehicle voice command systems, enhancing detection accuracy for drivers.
This paper tackled the problem of improving keyword spotting (KWS) in vehicles by integrating vehicle information like speed and direction to adjust system parameters, resulting in better precision, recall, and mean square error compared to a baseline system without this fusion.
In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multi-source fusion scheme in Keyword Spotting (KWS) System for vehicle applications. Vehicle information, as a new source for the original system, is collected by an in-vehicle data acquisition platform while the user is driving. A Deep Neural Network (DNN) is trained to extract acoustic features and make a speech classification. Based on the posterior probabilities obtained from DNN, the vehicle information including the speed and direction of vehicle is applied to choose the suitable parameter from a pair of sensitivity values for the KWS system. The experimental results show that the KWS system with the proposed multi-source fusion scheme can achieve better performances in term of precision rate, recall rate, and mean square error compared to the system without it.