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arXiv · 1902.04326

An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications

Yue Tan·Kan Zheng·Lei Lei
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Abstract

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.

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Yue Tan, Kan Zheng, Lei Lei. 2019-02-12. An In-Vehicle KWS System with Multi-Source Fusion for Vehicle Applications. https://arxiv.org/abs/1902.04326

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