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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Allen, Alyssa"],"institution":null,"degree_name":"Master of Arts (MA)","degree_level":"Open Access Thesis","degree_discipline":"English Language and Literature","degree_department":null,"school":null,"contributors":["Eric Acton, PhD","T. Daniel Seely, PhD"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-01T08:00:00Z","date_published":"2022-01-01T08:00:00Z","updated_at":"2026-07-24T02:17:47Z","subjects":["conversational machines","HMC","language and gender","sociolinguistics","sociophonetics","Linguistics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.emich.edu/theses/1171","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Eric Acton, PhD","T. Daniel Seely, PhD"]},{"key":"dc:creator","label":"Author","values":["Allen, Alyssa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2022-10-24T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["English Language and Literature"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Arts (MA)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["conversational machines","HMC","language and gender","sociolinguistics","sociophonetics","Linguistics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.emich.edu/theses/1171"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["A sociophonetic analysis of female-sounding virtual assistants"]}]}],"canonical_facts":{"dc:contributor":["Eric Acton, PhD","T. Daniel Seely, PhD"],"dc:creator":["Allen, Alyssa"],"dc:date.available":["2022-10-24T07:00:00Z"],"dc:description.abstract":["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.
"],"dc:identifier":["https://commons.emich.edu/theses/1171"],"dc:subject":["conversational machines","HMC","language and gender","sociolinguistics","sociophonetics","Linguistics"],"dc:title":["A sociophonetic analysis of female-sounding virtual assistants"],"thesis:degree_discipline":["English Language and Literature"],"thesis:degree_level":["Open Access Thesis"],"thesis:degree_name":["Master of Arts (MA)"]},"updated_at":"2026-07-24T02:17:47Z"}