Open Access Thesis
Master of Arts (MA)
Department or School
English Language and Literature
Eric Acton, PhD
T. Daniel Seely, PhD
As conversational machines (e.g., Apple's Siri and Amazon's Alexa) are increasingly anthropomorphized by humans and viewed as active interlocutors, it raises questions about the social information indexed by machine voices. This thesis provides a preliminary exploration of the relationship between human sociophonetics, social expectations, and conversational machine voices. An in-depth literature review (a) explores human relationships with and expectations for real and movie robots, (b) discusses the rise of conversational machines, (c) assesses the history of how female human voices have been perceived, and (d) details social-indexical properties associated with F0, vowel formants (F1 and F2), -ING pronunciation, and /s/ center of gravity in human speech. With background context in place, Siri and Alexa's voices were recorded reciting various sentences and passages and analyzed for each of the aforementioned vocal features. Results suggest that sociolinguistic data from studies on human voices could inform hypotheses about how users might characterize conversational machine voices and encourage further consideration of how human and machine sociophonetics might influence each other.
Allen, Alyssa, "A sociophonetic analysis of female-sounding virtual assistants" (2022). Master's Theses and Doctoral Dissertations. 1171.