
Disruptive situation detection on public transport through speech emotion recognition
We propose to frame disruptive situation detection as a speech emotion recognition task. To validate our hypotheses, we carry out an extensive experimental study focusing on the development of a model characterized by speaker/gender independence, robustness to noise, and robustness against multiple voices.