Me, Myself, and My Voice: Exploring Cultural and Linguistic Identity in AAC AI-generated Voices
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917526982623232 |
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| author | Weinberg, Tobias Lewis, Aaleyah Penuela, Ricardo E. Gonzalez Hong, Weicong Mankoff, Jennifer Roumen, Thijs |
| author_facet | Weinberg, Tobias Lewis, Aaleyah Penuela, Ricardo E. Gonzalez Hong, Weicong Mankoff, Jennifer Roumen, Thijs |
| contents | Voice is a central element of identity. We recognize people by their voice, and we uniquely express who we are with it. For people who rely on augmentative and alternative communication~(AAC) systems, such as speech-generating devices~(SGD), the device's voice becomes an identity marker others associate with them. Yet, it is hard to find a voice that truly aligns with one's identity both linguistically and culturally. Although modern AI-generated voices can reproduce diverse accents and speaking styles, AAC users still lack accessible ways to articulate how they want an identity-aligned voice to sound like. We first conducted a survey of AAC users (across eight countries) to characterize current voice representation, finding that non-binary, transgender, and non-US-born respondents rated their current voice support identity alignment consistently lower than other respondents. To examine how AAC users respond to voices designed to reflect their cultural identity, we built a tool that elicits cultural markers through guided questions and generates personalized voice candidates for participants to hear and reflect on. After participants heard the voices, we interviewed them to examine what it means for a voice to feel culturally representative, how they interpreted voices with cultural connotations, and how these voices shaped their sense of identity and agency. Our findings show that cultural voice alignment runs deeper than accent or language alone; it touches on belonging, self-recognition, and what it means to be heard as who you are. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24337 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Me, Myself, and My Voice: Exploring Cultural and Linguistic Identity in AAC AI-generated Voices Weinberg, Tobias Lewis, Aaleyah Penuela, Ricardo E. Gonzalez Hong, Weicong Mankoff, Jennifer Roumen, Thijs Human-Computer Interaction Voice is a central element of identity. We recognize people by their voice, and we uniquely express who we are with it. For people who rely on augmentative and alternative communication~(AAC) systems, such as speech-generating devices~(SGD), the device's voice becomes an identity marker others associate with them. Yet, it is hard to find a voice that truly aligns with one's identity both linguistically and culturally. Although modern AI-generated voices can reproduce diverse accents and speaking styles, AAC users still lack accessible ways to articulate how they want an identity-aligned voice to sound like. We first conducted a survey of AAC users (across eight countries) to characterize current voice representation, finding that non-binary, transgender, and non-US-born respondents rated their current voice support identity alignment consistently lower than other respondents. To examine how AAC users respond to voices designed to reflect their cultural identity, we built a tool that elicits cultural markers through guided questions and generates personalized voice candidates for participants to hear and reflect on. After participants heard the voices, we interviewed them to examine what it means for a voice to feel culturally representative, how they interpreted voices with cultural connotations, and how these voices shaped their sense of identity and agency. Our findings show that cultural voice alignment runs deeper than accent or language alone; it touches on belonging, self-recognition, and what it means to be heard as who you are. |
| title | Me, Myself, and My Voice: Exploring Cultural and Linguistic Identity in AAC AI-generated Voices |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2605.24337 |