Estimation of active speaker's direction using particle filters for in-vehicle environment

Abstract

Building in-vehicle human-machine interfaces, information on speaker's direction is helpful for achieving robust speech recognition and video camera control applications. This chapter presents two-step particle filtering approach in a spectro-spatial domain for achieving robust Direction-Of-Arrival (DOA) estimation in noisy environments such as in-vehicle conditions. The particle filter is applied to track the movement of an active speaker while speaking, and the two-step filtering aims at combining the advantages of both traditional cross-correlation (CC) and generalized cross-correlation (GCC) methods.