Papers
26
Total Citations
474
H-Index
12
About
Ilaria Torre is a prominent researcher working at the intersection of human-robot interaction, voice design, and artificial intelligence, with particular expertise in how vocal characteristics shape human perception of machines. Her work has fundamentally advanced our understanding of the acoustic and social dimensions of robot communication, accumulating over 368 citations across her most influential publications. Torre's most celebrated contribution, "Can you Tell the Robot by the Voice?" (2019, 110 citations), shifted scholarly attention toward the underexplored role of auditory cues in robot perception, complementing the field's traditional focus on visual aesthetics. Her parallel investigation into trust — explored in "Trust in Artificial Voices" (2018, 70 citations) and subsequent work on vocal human-robot interaction — has provided crucial design implications for engineers and developers building socially acceptable machines. Beyond voice, Torre has demonstrated a strong commitment to equity in research practice, critically examining gender representation among HRI study participants. Her work on gender-ambiguous robot voices further reflects her dedication to reducing harmful stereotypes in AI deployment. More recently, her editorial contributions on generative AI in education signal a broadening research horizon. Torre's scholarship consistently bridges cognitive science, linguistics, and robotics, making her an indispensable voice in shaping ethical, human-centred AI design.
Research Focus
Key Achievements
Top Papers
- 1
- 2Trust in artificial voices70 citations · 2018
- 3
- 4Trust in Vocal Human–Robot Interaction: Implications for Robot Voice Design28 citations · 2020
- 5How context shapes the appropriateness of a robot’s voice24 citations · 2020
- 6
- 7
- 8Should robots have accents?19 citations · 2020
- 9Editorial: Generative AI in education17 citations · 2024
- 10Robo-Identity17 citations · 2021