## 5 Discussion  
As a first observation, it can be noted how the audio-only approach to active speaker detection (Tables 2 and 3) performed surprisingly well for the AVA-ActiveSpeaker videos but was rather unsuccessful for the Columbia dataset. This is attributed to the mentioned data homogeneity of the latter, considering it encompasses a single speaker panel video. The fact that there is constant speech in the video (one speaker picks up after the other) prevents the model from learning even the most basic factor of discerning silence from speech. Hence results matching random decision for speaker dependency but failing with the independent case. Still regarding the Columbia dataset's homogeneity, it proves to be advantageous for the dependent section of the video-only testing phase, but again not so much for the independent one. Thus the model naturally performs better with previously seen subjects but fails to extrapolate suitable ASD features by training with such a reduced number of speakers. Not unlike the audio testing however, the AVA videos led to a considerably high performance using images only, proving data heterogeneity and speaker abundance to be ultimately better for training as expected. Audiovisual results greatly surpassed expectations, with the baseline concatenation approach already demonstrating state-of-the-art performance for either dataset considered. Increase in terms of standard deviation from the dependent to the independent testing was observed as expected, considering how the model suffers a natural decrease of confidence in its predictions when analyzing previously unseen speakers. Relatively poor results are of course still obtained for the independent part of the Columbia dataset testing due to the constant speech issue, which as expected severely hinders the model's performance. Nonetheless, results are remarkably positive for either concatenation or SCF layer approaches with respect to speaker dependent testing. Moreover, the SCF layer still showed its superiority by majorly improving over concatenation and even surpassing a random choice performance despite having to deal with crippling audio data. As for the AVA video testing, both the concatenation and SCF results beat those presented in the \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Speaker Dependent** & **Speaker Independent** \\ \hline \(Concat+MLP_{3}\) (Baseline) & 97.60 (0.49) [\%] & 41.78 (15.46) [\%] \\ \(SCF+MLP_{3}\) (New Approach) & 98.53 (0.21) [\%] & 58.50 (7.71) [\%] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Audiovisual results using the Columbia dataset with respect to Mean Accuracy (Standard Deviation). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Video Dependent** & **Video Independent** \\ \hline \(Concat+MLP_{3}\) (Baseline) & 89.10 (0.41) [\%] & 87.52 (2.49) [\%] \\ \(SCF+MLP_{3}\) (New Approach) & 91.68 (0.51) [\%] & 88.46 (2.06) [\%] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Audiovisual results using the AVA-ActiveSpeaker videos with respect to Mean Accuracy (Standard Deviation). dataset paper [25], because even though testing was carried out with only 20 of the original videos, the performance gain was still exemplary in terms of result excellence. Even more so, the SCF layer successfully improved the already excellent baseline performance. This serves to validate its bio-inspired concept--mutual neuron excitation of spatially proximal multi-modal neural regions for integration of uni-sensory information--here emulated for modality fusion. Undoubtedly even greater performance gains could be achieved if all the layer's hyperparameters were finely tuned to our application, rather than using default values. Nonetheless, results were highly successful and warrant further research in this and other areas encompassing several types of modalities. Finally, the full model performed quite well on a snippet of novel data not part of either datasets used during training and testing.