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arxiv: 2605.24337 · v1 · pith:SQUGVRDYnew · submitted 2026-05-23 · 💻 cs.HC

Me, Myself, and My Voice: Exploring Cultural and Linguistic Identity in AAC AI-generated Voices

Tobias Weinberg , Aaleyah Lewis , Ricardo E. Gonzalez Penuela , Weicong Hong , Jennifer Mankoff , Thijs Roumen This is my paper

Pith reviewed 2026-06-30 13:01 UTC · model grok-4.3

classification 💻 cs.HC
keywords AACvoice identitycultural alignmentAI voicesassistive technologylinguistic identityuser agencyidentity representation
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The pith

AAC users experience deeper identity alignment from culturally matched AI voices than from accent or language matching alone.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper surveys AAC users across eight countries and identifies gaps in voice support, particularly for non-binary, transgender, and non-US-born individuals. It introduces a tool that uses guided questions to identify cultural markers and generates corresponding AI voice candidates. Through follow-up interviews, the study explores how these voices influence users' perceptions of belonging and self-recognition. The central insight is that cultural voice alignment involves more than linguistic features and affects users' sense of agency and identity.

Core claim

The study demonstrates that for people using AAC systems, a voice that aligns with their cultural identity extends beyond reproducing accent or language; it engages with feelings of belonging, self-recognition, and the experience of being heard as one's authentic self, as revealed through participant responses to personalized voice candidates.

What carries the argument

A custom tool that elicits cultural markers through guided questions and generates personalized AI-generated voice candidates for participants to evaluate.

If this is right

  • Non-binary, transgender, and non-US-born AAC users rate their current voice support for identity alignment lower than other groups.
  • Voices designed with cultural markers lead to reflections on personal identity and agency during interviews.
  • Current AI voice technologies do not fully address the cultural aspects of voice identity for AAC users.
  • Accessible methods for users to specify cultural voice preferences can improve representation in speech-generating devices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers of assistive voice technologies might benefit from incorporating similar elicitation methods to better serve diverse users.
  • Testing these voices in real-world social settings could reveal impacts on communication effectiveness and social inclusion.
  • Similar approaches could apply to voice design for other groups with identity-related communication needs.

Load-bearing premise

The guided questions accurately identify cultural markers that will be perceived by users as representative when translated into generated voices.

What would settle it

If participants in a controlled test rate the tool-generated voices as no more culturally aligned than voices matched only by accent or randomly selected, the finding that cultural alignment runs deeper would be challenged.

Figures

Figures reproduced from arXiv: 2605.24337 by Aaleyah Lewis, Jennifer Mankoff, Ricardo E. Gonzalez Penuela, Thijs Roumen, Tobias Weinberg, Weicong Hong.

Figure 1
Figure 1. Figure 1: We explore cultural identity in AAC voice generation, participants in our study reported several forms of misalignment, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Demographic distribution of the 53 validated survey [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Mean ratings (1–5) across survey dimensions com [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The cultural probing tool used in Task 2. (a) Partici [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Participant-level evaluations of Voice 1 and Voice 2 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper reports a survey of AAC users across eight countries showing lower identity alignment ratings for non-binary, transgender, and non-US-born respondents compared to others. It describes development of a custom tool that elicits cultural markers through guided questions to produce personalized AI-generated voice candidates. Interviews with participants who heard these voices then explore perceptions of cultural representation, leading to the claim that cultural voice alignment extends beyond accent or language to include belonging, self-recognition, and agency.

Significance. If substantiated, the work makes a meaningful contribution to HCI and accessibility research by shifting focus from technical voice synthesis to sociocultural dimensions of identity in AAC systems. The mixed-methods design, combining a multi-country survey with targeted interviews, offers user-centered insights that could guide more inclusive voice design practices. The custom tool represents an innovative attempt to bridge user input with generative AI, though its effectiveness requires further substantiation.

major comments (2)
  1. [Tool Development and Voice Generation (Methods)] The interview-based findings on belonging and self-recognition rest on the unvalidated assumption that the custom tool's guided questions produce voices that faithfully render the elicited cultural markers. The manuscript provides no pilot testing, expert review, or fidelity assessment (e.g., quantitative comparison of input markers to output voice parameters or user perception checks) to confirm this mapping. This is load-bearing for the central claim, as interview themes could instead reflect reactions to generation artifacts.
  2. [Survey Results] Survey results on group differences in identity alignment ratings lack reported sample sizes, recruitment details, response rates, or the specific statistical tests and effect sizes used for comparisons. These omissions undermine evaluation of whether the reported disparities are robust enough to support the subsequent design of the tool and interviews.
minor comments (2)
  1. [Abstract] The abstract would benefit from including participant numbers for both the survey and interviews to provide immediate context for the scale of the study.
  2. [Interview Analysis (Methods)] Interview analysis methods (e.g., thematic analysis approach, coding process, or inter-rater reliability if applicable) are not described, which affects transparency of how themes on cultural representation were derived.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments on our manuscript. We have carefully considered each point and provide point-by-point responses below, noting revisions where appropriate.

read point-by-point responses

  1. Referee: [Tool Development and Voice Generation (Methods)] The interview-based findings on belonging and self-recognition rest on the unvalidated assumption that the custom tool's guided questions produce voices that faithfully render the elicited cultural markers. The manuscript provides no pilot testing, expert review, or fidelity assessment (e.g., quantitative comparison of input markers to output voice parameters or user perception checks) to confirm this mapping. This is load-bearing for the central claim, as interview themes could instead reflect reactions to generation artifacts.

    Authors: We acknowledge that the manuscript does not report formal pilot testing, expert review, or quantitative fidelity assessments of the custom tool. As an exploratory study, the tool was designed iteratively to facilitate user reflection on cultural identity in voice generation, and the interview data captures participants' subjective perceptions of the generated voices. However, we agree that this assumption is important to address. In revision, we will expand the Methods section to detail the tool's development process, including how questions were derived from the survey findings and any informal validation steps taken during interviews. We will also add a limitations subsection explicitly noting the absence of formal fidelity checks and discussing how this affects interpretation of the findings. This represents a partial revision as we cannot retroactively add new empirical validation data. revision: partial

  2. Referee: [Survey Results] Survey results on group differences in identity alignment ratings lack reported sample sizes, recruitment details, response rates, or the specific statistical tests and effect sizes used for comparisons. These omissions undermine evaluation of whether the reported disparities are robust enough to support the subsequent design of the tool and interviews.

    Authors: We apologize for the omission of these methodological details in the submitted manuscript. The survey involved AAC users recruited through international organizations and online communities in eight countries. We will revise the manuscript to include the exact sample size, detailed recruitment procedures, response rates, and a full description of the statistical methods, including the tests applied for group comparisons and associated effect sizes. This will strengthen the transparency and allow better evaluation of the survey findings. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical findings from survey and interviews stand independently

full rationale

The paper reports results from a survey of AAC users and subsequent interviews after exposure to a custom tool that generates voice candidates. No equations, fitted parameters, derivations, or self-citations appear in the load-bearing claims. The central findings (cultural alignment involving belonging and self-recognition) are presented as emerging from participant reflections on the generated voices rather than being defined into existence or forced by prior self-referential results. The absence of validation for the tool's mapping is a potential correctness issue but does not constitute circularity under the specified patterns, as the work does not reduce any prediction or claim to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

No free parameters or invented entities. Relies on standard qualitative assumptions about self-reported data.

axioms (1)
  • domain assumption Self-reported feelings from interviews reliably reflect users' sense of cultural identity alignment with voices.
    Central to interpreting interview data as evidence of deeper cultural representation.

pith-pipeline@v0.9.1-grok · 5801 in / 1114 out tokens · 34503 ms · 2026-06-30T13:01:57.322391+00:00 · methodology

discussion (0)

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