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

## TL;DR

This paper introduces a multi-source fusion approach for in-vehicle keyword spotting that combines vehicle data and acoustic analysis to improve detection accuracy during driving.

## Contribution

It presents a novel fusion scheme integrating vehicle information with DNN-based speech recognition to enhance KWS performance in vehicle environments.

## Key findings

- Improved precision and recall rates in KWS detection.
- Enhanced system robustness with multi-source data fusion.
- Reduced mean square error compared to baseline systems.

## 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.