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SPEAKER LOCALIZATION EXPLOITING SPATIAL-TEMPORAL INFORMATION

Determining the spatial position of a speaker finds a growing interest in video conference scenario where automated camera steering and tracking are required. Speaker localization can be achieved with a dual step approach. In the preliminary stage microphone array is used to extract the time difference of arrival (TDOA) of the speech signal. These readings are then used by the second stage for the actual localization. Since speaker trajectory must be smooth, estimates of close speaker positions might be used to improve the current position estimate. However, many methods, although exploiting the spatial information obtained by dierent microphone pairs, do not exploit this temporal information. In this contribution we present two localization schemes, which exploit the temporal information. The first is the well known extended Kalman filter (EKF). The second is a recursive form of a Gauss method, which we denote Recursive Gauss (RG). Experimental study supports the potential of the proposed methods.