Automatic translation tools have facilitated navigating multilingual contexts, by providing accessible shortcuts for gathering, processing, and spreading information. As language technologies become more widely used and deployed on a large scale, however, their societal impact has sparked concern both within and outside the research community. This thesis adresses gender bias affecting Machine Translation (MT) and Speech Translation (ST) models. It contributes to this pressing area of research with an interdisciplinary perspective, to raise awareness of bias, improve the understanding of the phenomenon, and investigate best practices and methods to unveil and mitigate it in translation systems.

Previous research suggests that gender-appropriate and gender-inappropriate use of sex-linked linguistic markers alters subjects' perceptions of the speaker. The present study examined the effects of male and female speech styles on clients' perceptions,. Undergraduates (N = 160) listened to audiotapes of clinical psychologists introducing the same client to psychotherapy. Clinician gender and sex-linked linguistic markers were manipulated. The results suggested that sex-stereotypes of males, females, and occupations played an important role in altering clients' perceptions of clinical psychologists. Sex-stereotypes did not, however, determine the desirability of the speaker as a therapist. The use of female speech styles increased the clinician's perceived femininity and desirability as a therapist.