This paper presents techniques that enable talker tracking for effective human-robot interaction. To track moving people in daily-life environments, localizing multiple moving sounds is necessary so that robots can locate talkers. However, the conventional method requires an array of microphones and impulse response data. Therefore, we propose a way to integrate a cross-power spectrum phase analysis (CSP) method and an expectation-maximization (EM) algorithm. The CSP can localize sound sources using only two microphones and does not need impulse response data. Moreover, the EM algorithm increases the system's effectiveness and allows it to cope with multiple sound sources. We confirmed that the proposed method performs better than the conventional method. In addition, we added a particle filter to the tracking process to produce a reliable tracking path and the particle filter is able to integrate audio-visual information effectively. Furthermore, the applied particle filter is able to track people while dealing with various noises that are even loud sounds in the daily-life environments.