Emotional contagion in consecutive interpreting: An empirical study with novice interpreters

It has recently been noticed that interpreters tend to converge with their speakers’ emotions under a process known as emotional contagion. Emotional contagion still represents an underinvestigated aspect of interpreting and the few studies on this topic have tended to focus more on simultaneous interpreting rather than consecutive interpreting. Korpal & Jasielska (2019) compared the emotional effects of one emotional and one neutral text on interpreters in simultaneous interpreting and found that interpreters tended to converge emotionally with the speaker more when interpreting the emotional text. This exploratory study follows their procedures to study the emotional contagion potentially caused by two texts among interpreters in consecutive interpreting: one emotionally neutral text and one negatively-valenced text, this last containing 44 negative words as triggers. Several measures were triangulated to determine whether the triggers in the negatively-valenced text could prompt a stronger emotional contagion in the consecutive interpreting of that text as compared to the consecutive interpreting of the emotionally neutral text, which contained no triggers—namely, the quality of the interpreters’ delivery; their heart rate variability values as collected with EMPATICA E4 wristbands; the analysis of their acoustic variations (i.e., disfluencies and rhetorical strategies); their linguistic and emotional management of the triggers; and their answers to the Italian version of the Positive and Negative Affect Schedule (PANAS) self-report questionnaire. Results showed no statistically significant evidence of an emotional contagion evoked by the triggers in the consecutive interpreting of the negative text as opposed to the consecutive interpreting of the neutral text. On the contrary, interpreters seemed to be more at ease while interpreting the negative text. This surprising result, together with other results of this project, suggests venues for further research.

Model-Based validation of Driver Drowsiness Detection System for ADAS

The work described in this Master’s Degree thesis was born after the collaboration with the company Maserati S.p.a, an Italian luxury car maker with its headquarters located in Modena, in the heart of the Italian Motor Valley, where I worked as a stagiaire in the Virtual Engineering team between September 2021 and February 2022. This work proposes the validation using real-world ECUs of a Driver Drowsiness Detection (DDD) system prototype based on different detection methods with the goal to overcome input signal losses and system failures. Detection methods of different categories have been chosen from literature and merged with the goal of utilizing the benefits of each of them, overcoming their limitations and limiting as much as possible their degree of intrusiveness to prevent any kind of driving distraction: an image processing-based technique for human physical signals detection as well as methods based on driver-vehicle interaction are used. A Driver-In-the-Loop simulator is used to gather real data on which a Machine Learning-based algorithm will be trained and validated. These data come from the tests that the company conducts in its daily activities so confidential information about the simulator and the drivers will be omitted. Although the impact of the proposed system is not remarkable and there is still work to do in all its elements, the results indicate the main advantages of the system in terms of robustness against subsystem failures and signal losses.