## 6 Conclusions  
This work proposed a novel modality fusion layer (SCF), bio-inspired by the brain's superior colliculus region and incorporated into a new method for ASD in real-world scenarios. This proposed method deals with audio and video information jointly and makes no assumptions regarding input data. The audiovisual technique was extensively tested against data from two tried and tested ASD databases, specifically the Columbia [15] and the AVA-ActiveSpeaker [25] datasets. The obtained results were compared to those of audio-only and video-only approaches, as well as those of a naive concatenation multi-modal baseline. In any case the SCF technique always demonstrated state-of-the-art performance, surpassing its baseline and uni-modal counterparts. It was shown how mutual neuron excitation of a spatially proximal multi-modal region by uni-modal neurons can be a remarkably successful modality fusion technique.  
Figure 4: Performance of the trained full model on unseen data: (**a**) Single speaker scenario. (**b**) Multiple speaker scenario.  
This observed SCF layer's success in terms of uni-sensory data integration, even despite doing so with default settings, evidently warrants a greater exploration of its capabilities. As such, we further intend to assess whether the increased performance of the SCF layer w.r.t. simple concatenation is even more accentuated when one of the modalities is missing. In addition, we will examine the layer's behavior in terms of not only audiovisual fusion but also combination of data of other natures (e.g., tactile).  
Conceptualization, G.A., N.G. and P.M.; methodology, G.A., N.G. and P.M.; software, G.A.; validation, G.A.; data curation, G.A.; writing--original draft preparation, G.A.; writing--review and editing, N.G. and P.M.; supervision, P.M.; All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by scholarship 2020.05620.BD of the Fundacao para a Ciencia e a Tecnologia (FCT) of Portugal, and OE--National funds of FCT/MCTES under project number UIDB/00048/2020.  
Not applicable.  
Publicly available datasets were analyzed in this study. The Columbia dataset video can be found in [[https://youtu.be/6](https://youtu.be/6) Gzxbr00 DHM]([https://youtu.be/6](https://youtu.be/6) Gzxbr00 DHM). Annotations are available at [[http://www.jaychakravarty.com/wp-content/uploads/2016/02/columbiaDatasetGroundTruth.zip](http://www.jaychakravarty.com/wp-content/uploads/2016/02/columbiaDatasetGroundTruth.zip)]([http://www.jaychakravarty.com/wp-content/uploads/2016/02/columbiaDatasetGroundTruth.zip](http://www.jaychakravarty.com/wp-content/uploads/2016/02/columbiaDatasetGroundTruth.zip)). The full AVA-ActiveSpeaker dataset can be found in [[https://research.google.com/ava/download.html#ava_active_speaker_download](https://research.google.com/ava/download.html#ava_active_speaker_download)]([https://research.google.com/ava/download.html#ava_active_speaker_download](https://research.google.com/ava/download.html#ava_active_speaker_download)). The list of the 20 AVA-ActiveSpeaker videos analyzed in this study is avaiable at [[https://github.com/gustavomiguelsa/SCF](https://github.com/gustavomiguelsa/SCF)]([https://github.com/gustavomiguelsa/SCF](https://github.com/gustavomiguelsa/SCF)). No new data were created in this study.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ASD & Active speaker detection \\ VAD & Voice activity detection \\ TDOA & Time difference of arrival \\ CNN & Convolutional neural network \\ LSTM & Long short term memory \\ RN-LSTM & Resnet-LSTM \\ SC & Superior colliculus \\ SCF & Superior colliculus fusion \\ \end{tabular}