Tan, Yue, Zheng, Kan, and Lei, Lei (2019) An in-vehicle keyword spotting system with multi-source fusion for vehicle applications. In: Proceedings of the IEEE Wireless Communications and Networking Conference. 8885980. From: WCNC 2019: IEEE Wireless Communications and Networking Conference, 15-18 April 2019, Marrakesh, Morocco.

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Abstract

In order to maximize detection precision rate as well as the recall rate, this paper proposes an in-vehicle multisource 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.

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