An in-vehicle keyword spotting system with multi-source fusion for vehicle applications

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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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.

Item ID: 61847
Item Type: Conference Item (Research - E1)
ISSN: 1558-2612
Keywords: multi-source fusion, sensitivity value, spotting, vehicle information
Copyright Information: © 2019 IEEE.
Funders: National Natural Science Foundation of China (NNSFC)
Projects and Grants: NNSFC 61671089
Date Deposited: 21 May 2020 04:31
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 100%
Downloads: Total: 1
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