## 1 Introduction  
To achieve natural and effective human-robot Interaction (HRI), robots need to utilize multiple senses to communicate with humans. Among these senses, robot audition has become increasingly important in recent years. The concept of robot audition was first proposed by [PERSON] et al. [1] and is defined by three key abilities, (1) the ability to locate sound sources in unknown acoustic environments, (2) the ability to move actively to obtain more information about the sound and (3) the ability to continuously perform acoustic scene analysis in noisy environments. Current research on robot audition focuses on Sound Source Localization (SSL) [2], Sound Source Separation (SSS) [3] and Automatic Speech Recognition (ASR) [4]. While these techniques have become increasingly mature in single-speaker scenarios after more than two decades of development, locating and tracking a particular speaker in a noisy environment where multiple speakers are communicating simultaneously as illustrated in Figure 1 remains a challenge, and is known as the cocktail party problem [5]. This problem involves two challenges [6], how to locate the sound of interest and how to collect a clear audio signal of the target from a variety of background noises. The first challenge can be solved via SSL. At present, traditional SSL methods have the ability to locate the sound sources relying only on microphone arrays [7; 8]. However, when the sound sources are far from the robots compared to the aperture of the microphone array (called far-field effects), these SSL methods cannot estimate the distance between the sources and the robots [9]. To deal with this problem, more and more studies attempt to combine visual Simultaneous Localization and Mapping (SLAM) with SSL [9; 10; 11; 12], which can provide the relative positions of the sources and the robots. Although these SLAM-based SSL approaches can accurately estimate the locations of sound sources, they cannot determine which source a particular sound is emitted from. Specifically, suppose there are two people in a room, one is singing and the other is calling out. The above-mentioned SLAM-based SSL methods can estimate the relative position between these two active sound sources and the robot, but they do not know who is singing and who is making a phone call. Therefore, these techniques cannot be applied directly to resolve the cocktail party problem where the robot should locate the source of interest such as the one that is singing rather than all sound sources in the environment. To meet the second challenge, there has been an amount of work investigating speech separation in cocktail party environments [3, 13, 14, 15] and the most advanced techniques have been able to separate clear speech from mixed signals [3]. However, because of the front-back ambiguity of binaural arrays [16, 17], these methods trained with binaural videos cannot be directly applied in a 3D cocktail party environment. Furthermore, most of them ignore the ability of robots to move actively to acquire more information about the target. In this paper, we attempt to fill the aforementioned research gaps to some extent. We propose AV-SSL (short for Audio-visual Sound Source Localization and Tracking), a framework that uses voiceprint and face features to identify the target speaker and locate her/his position and collects clear audio data of the target by moving towards it while staying away from other sound sources in the surroundings controlled via the motion module. The characteristics of AV-SSL and our contributions can be summarized as follows:  
* To identify and localize a specific sound source in a cocktail party environment, we propose an audio-visual approach since the vision can help the audition to distinguish the target speaker. Considering that voiceprint recognition and face recognition technologies are relatively mature, AV-SSL combines these two types of techniques to find out the specific sound source and its position using microphones and RGB-D cameras.