## 1 Introduction  
The ability to discern between activity and passivity in humans may well benefit social robots and autonomous cognition systems by enabling a more accurate and natural response in interactive scenarios. Accordingly, machines capable of this task would be able to measure a user's engagement level and infer valuable conclusions. Moreover, successfully solving this task would expedite a research shift towards other problems whose solutions depend on the output of a human activity-passivity recognizer. Recent work on data mining and collection [1] and speaker diarization [2], which heavily rely on the use of active speaker detection, are exemplary of the topic's importance. Active speaker detection (ASD), or the ability to recognize who in a group is speaking at any given time, is a subset of human activity-passivity recognition. This subset focuses on mapping utterances that make up a speech signal, such as in a conversation, to either a closed or open set of intervening speakers. In turn, techniques from the subset may take advantage of audio-only, visual-only or audiovisual information depending on their nature. Though studies do exist taking advantage of other types of perceptual input, such as [3], these are far more uncommon and somewhat less successful. In the past, extensive research effort has been put towards the audio-only section of ASD as a side tool for other problems [4, 5]. Techniques generally relying on sequential speaker clustering [6] or speech source localization [7, 8], were often enough to achieve real-time diarization, some occasionally aided by supervised learning. Unfortunately these were constrained by predetermined assumptions--mic location, static speakers, controlled environment. Furthermore, audio-only approaches remain unable to match segmented and clustered speech with visual speakers without additional data or restrictions, limiting their use on audiovisual data. Hence, visual approaches to the topic become useful, though not without their own drawbacks. Unreservedly interpreting lip movement [9], or other facial feature variations [10, 11], as vocal activity without accounting for similar motions stemming from normal human interaction--emotional expression, coughing, chewing--will of course negatively impact the detection process. An exemplary output of an active speaker detection system is shown in Figure 1, for easier understanding. In terms of multi-modal approaches, by means of complementing a visual approach with its audio counterpart or vice-versa, performances are generally higher thanks to increased robustness. This improvement is obtained by exploiting correlations between the two types of data (e.g., [12, 13], which may decrease ambiguous situations prone to cause model confusion--speaker overlap, unrelated movement. Self-evidently, techniques requiring both a visual and audio input are unable to deal with situations where either of the modalities is missing. These situations however are scarce, an do not correspond to the immensely vast amount of audiovisual data available nowadays. Thus multi-modality remains as the preferred manner of addressing active speaker detection [14, 15, 16]. In spite of the recent technological advancements which have allowed deep learning research to flourish, such improvements would likely not have been possible without key biological contributions to the area. The success of remarkable architectures such as convolutional neural networks, whose earliest iterations [17] strived to emulate neuron behavior in the visual cortex of primates [18], would likely have been halted without this biological inspiration. However, engineering maintains a collaborative yet stubbornly segregated relationship with biology by disregarding several other promising models which the latter has to offer. In this study our contribution is two-fold as we propose a new multi-modal approach to visual ASD as well as a novel bio-inspired modality fusion layer which strives to emulate the superior colliculus structure of the brain.