Abstract
Disruptive situations are emotionally-charged events diverging from ordinary behavior, like people fighting or screaming. Public transports are one type of social environment where disruptive situation may occur, and their timely detection may bring significant improvements to people’s safety. Current approaches to disruptive situation detection, typically based on CCTVs, do not take the emotional dimension into account. Conversely, we propose to frame such a problem 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. We investigate a variety of audio features, classifiers, datasets, and data augmentation methods in an effort to define effective ways to address this under-investigated yet socially significant problem. Our experiments show that the proposed systems attain an F1 score of over 90% on the disruptive class, even when introducing noisy elements such as environmental noise or multiple overlapping voices. This robust performance is achieved with datasets characterized by speaker variability, gender diversity, and varying number of samples. Such promising results indicate that framing disruptive situation detection as a speech emotion recognition task could pave the way to the adoption of new types of intelligent systems with a positive impact on public safety.
Eleonora Mancini
Postdoctoral Research Fellow
Her research concerns artificial intelligence and in particular multimodal deep learning, natural language processing, image and speech recognition.
Andrea Galassi
Junior Assistant Professor
He is an expert in deep learning architectures for natural language processing.
Federico Ruggeri
Postdoctoral Research Fellow
His research aims to devise Natural Language Processing (NLP) systems that learn to generate, distill, and use knowledge from unstructured text.
Paolo Torroni
Associate Professor
Head of the Language Technologies lab. His main research focus is in artificial intelligence, and in particular natural language processing, multi-agent systems, and computational logics.