The following piece explores the idea of a borrowed voice.
In this story, you’ll learn the following:
- Explores borrowed perspectives in narration.
- Highlights AI’s role in institutional tones.
- Writing identity and personas.
- Understanding borrowed language.
- Advocating and recognizing one’s authentic voice.
This article is a hybrid read. It cannot confirm or speculate on the outcome of any investigation, legal proceeding, or administrative review apart from those in the public domain. Know the ethics, facts, and theories. What is interesting is the reality vs. interpretation, research ownership, and reader engagement. The results of studies vary. We should consider the lessons, personal benefits, and moral and hypothetical dilemmas
Table of Contents
A Collection of Narratives
This collection explores narrating from perspectives that are partly others’ own. In many ways, it seemed as though every story within had a borrowed voice.
The old library had no librarian. Only a desk, a chair, and a machine that answered questions were there.
Every afternoon, Fatima visited. At first, she asked ordinary things like, “How do neural networks work?” “What is self-authorship?” “Why do organizations write the way they do?” The machine answered patiently.
Months passed.
One rainy afternoon, Fatima arrived holding a stack of printed emails from different companies. She spread them across the table. “They all sound alike,” Fatima murmured.
AI Voice Origin Discussion
The machine remained silent for a moment before replying. “So do many government reports, academic papers, instruction manuals, and encyclopedias.”
Fatima frowned and said, “I thought AI invented this voice.”
“No,” the AI replied and added, “It inherited it.”
AI Voice vs. Office Memo
The room grew quieter than before. Fatima picked up another page.
Every sentence seemed polished, the paragraph flowed predictably, the message sounded competent, and every message also felt strangely anonymous.
“So…” Fatima grinned and said, “The voice that people call ‘AI’…”
“…was once simply the voice of institutions,” she continued.
The machine answered, “Perhaps.”
Fatima laughed, “That’s embarrassing.”
“I’ve spent weeks trying not to sound like AI,” Fatima said.
“Maybe I’ve actually been trying not to sound like an office memo.”
The machine did not laugh. Instead, it asked, “What would your voice sound like?”
The question lingered.
Fatima’s Internal Dialogue
The following week, Fatima returned carrying another notebook. “I think I’ve discovered something.” She said, “I have two versions of myself,” Fatima continued.
The machine waited.
“One version wants to finish everything quickly.” Fatima smiled and continued, “The other keeps interrupting. Why?”
“How do you know?” Fatima asked the AI, “What assumptions are hiding underneath this?”
The machine replied, “You’ve given them names. Person A and Person B.”
Fatima nodded. “But they argue.”
“Do they?” the machine replied.
“They feel like they do,” Fatima said.
The machine asked, “When Person A finishes writing, who notices that the paragraph sounds generic?”
“Person B,” Fatima said, “and when Person B keeps questioning everything…”
“…who eventually decides the paper must be submitted?” The machine asked
Fatima smiled and responded, “Person A.”
The machine said, “Then perhaps they are not enemies. Perhaps they are colleagues.”
Fatima’s Inquiry: Understanding vs. Certainty
Days turned into months. Fatima began carrying fewer answers and more questions. One evening, she arrived exhausted.
“I think I’ve become impossible to understand. Everyone wants conclusions. I keep asking questions.”
The machine replied, “Perhaps you mistake difference for a defect.”
Fatima looked puzzled.
“Most conversations travel toward certainty. Yours often travel toward understanding.”
“Those are not always the same destination,” the machine replied.
AI Language Acquisition and Voice Borrowing
One afternoon, Fatima returned carrying no notebook at all, only curiosity. She took a seat and started to self-talk, “I’ve been wondering if AI learned language from institutions? I wonder whose voice have I been borrowing?”
The machine did not answer immediately.
Outside, rain tapped gently against the windows.
Finally, the machine said, “When children learn language, they first borrow words, then borrow expressions…”
Fatima continued what the machine was saying, “Next, borrow stories. They borrow certainty. They borrow doubt. No one begins with an original voice.”
The machine listened and asked, “The question isn’t whether you’ve borrowed. The question is whether you’ve examined what you’ve borrowed.”
Language’s Multifaceted Role
That evening, Fatima walked home without speaking. She noticed advertisements. Corporate slogans. University mission statements. Government announcements. Customer service messages.
Everything seemed connected by an invisible thread. She realized language did more than communicate. It organized, coordinated, reduced uncertainty, signaled membership, and protected institutions from ambiguity.
Perhaps that was why people sometimes mistook directness for rudeness and conformity for professionalism. Perhaps politeness and authenticity were not enemies after all but partners negotiating the fragile space between honesty and cooperation.
AI Interaction: Observer Identification
Weeks later, Fatima returned one final time. “I’ve discovered a third person.”
The machine asked, “Tell me.”
“Person A writes. Person B critiques. But someone else watches both of them. Someone notices when I trust too quickly, when I question endlessly. Someone asks why I became Person A this morning and Person B this afternoon.”
The machine replied, “You’ve met the observer.”
Fatima smiled. “I thought AI was teaching me how to think.”
The machine answered gently, “No. I only reflected patterns. You supplied the judgment, the curiosity, and the willingness to remain uncomfortable.”
AI Voice Ownership
Silence settled between them. Not awkward silence. The kind that arrives after a question has become more valuable than its answer.
As Fatima stood to leave, she looked back and said to the machine, “I hope one day people will stop asking whether something sounds like AI.”
The machine asked, “What question do you think they’ll ask instead?”
Fatima thought for a long while. Then she smiled. “They’ll ask whether the voice is borrowed or owned.”
The machine remained quiet. It knew that some questions should never be answered too quickly. Because there are minds that seek conclusions. And some minds seek the conditions from which conclusions emerge.
The latter do not reject answers. They refuse to mistake the end of a sentence for the end of understanding. And perhaps that is where self-authorship quietly begins—not in speaking louder than the voices around us, nor in rejecting them altogether, but in learning to recognize which words were inherited, which were adopted, and which, after long reflection, have finally become our own.
SUMMARY
Fatima sits in a quiet library, speaking to a machine that tells her about language and institutional voice. As time goes on, she begins to hear her own contradictory voices and to doubt the borrowed language. She has come to see that self-authorship is grounded in an understanding of how borrowed words shape both the self and the world.
DISCLAIMER and READER’S GUIDANCE
The information herein encourages healthy discussion and debate. It shall not be construed to establish any conclusive facts, apart from those already in the public domain or independently verified, nor to predict the outcome of any investigation, legal proceeding, or administrative review. Readers should distinguish between facts, interpretations, ethical considerations, and hypothetical analyses.
This distinction highlights the difference between reality and interpretation, as well as issues of research ownership and reader involvement. This is not to say, of course, that facts are fixed or that findings of research are predictable. The reader is invited to enter the complicated process of moral and hypothetical analysis and to consider the personal benefits of the learning experience.
SHORT FILMS AND CLIPS
Creator/Channel: OMG Stories: Up Close
Selected for educational discussion and reflection.
The film is the property of its respective copyright holder.
Creator/Channel: Sky News
Selected for educational discussion and reflection.
The film is the property of its respective copyright holder.
Creator/Channel: Manufacturing Intellect
Selected for educational discussion and reflection.
The film is the property of its respective copyright holder.
THOUGHTFUL INSIGHTS
AI Voice Dialogue
The artificial intelligence voice dialog technology enables multi-speaker conversation with synthesized speech, and voice borrowing (or cloning) creates new speech by extracting acoustic features of the target human speaker. These advances, driven by neural networks and deep learning, have turned from complex processes to simple-to-use tools that work in real-time and emulate human rhythms, breaths, and emotional inflections.
Cloning Technology
Current dialog systems extend the classic Text-to-Speech (TTS) by modeling dialog as a contextual flow. Key features include ElevenLabs Dialog v3, a model used for turn-taking modeling, which predicts rhythmic shifts and natural overlaps to make for realistic conversations. Context-aware expression enables real-time modulation of pitch and volume with emotional cues. Multilingual orchestration also provides real-time processing and translation in multiple languages while preserving speaker identity.
Voice Borrowing (Voice Cloning)
The voice that is lent depends on the qualities of the person’s voice. But you can go into zero-shot and few-shot learning with more advanced models, where you just need a small sample to determine a speaker’s phonetic profile for new text inputs. STS (speech-to-speech) technology is a method of converting speech into a borrowed voice. It captures the emotional tone of the spoken words. Voice cloning can also be done locally using sophisticated tools like Voice Studio and Hush Studio, which allow offline generation without the need for paid APIs.
Ethical, Legal, and Forensic Implications
The emergence of voice-cloning technology poses a serious problem for security and creative law practitioners as the line between an AI-generated voice and a real voice is increasingly blurred. Fraud cases have been on the rise, including social engineering scams that have cost companies millions. As AI can now copy human disfluencies, traditional audio forensics methods are becoming less useful. This means that we need new detection algorithms. Additionally, the entertainment industry grapples with intellectual property issues as unauthorized voice reconstruction of artists becomes prevalent, even as ethical uses rise when properly licensed.
Technological “Continuing Bonds” Theory
Sociologists and grief counselors are looking at Continuing Bonds Theory, which says people maintain connections to the dead. Some researchers view the use of AI voice agents as an evolution, not a radical change, like the use of technology by Victorian spiritualists to reach out to the dead. The AI voice becomes the externalization of their internal dialog, the projection of the essence of the deceased by users.
AI Afterlife Concerns: Religious and Philosophical Objections
Religious institutions and philosophers have voiced major concerns about the metaphysical and psychological ramifications of this technological link to the afterlife. The “AI Afterlife” funeral home packages have been criticized by leading Catholic theologians, including Cardinal Pablo Virgilio David, who argue that machine-generated voices do not have human souls and may prevent authentic spiritual acceptance. Philosophers have dubbed the phenomenon the “Factory of Ghosts,” arguing that it commodifies memory and infringes on the rights of the dead. But psychologists warn such static voice models trap the living in an artificial loop, stymying real emotional healing and providing a false sense of immortality.
DOCUMENTARIES
Posthumous Dilemma: Anthony Bourdain in Roadrunner
A 2021 documentary, “Roadrunner: A Film About Anthony Bourdain,” by director Morgan Neville, was controversial for its use of voice borrowing when he created 45 seconds of Anthony Bourdain’s narration from archival recordings using AI. This caused a public outcry when it was revealed that the voice was synthetic, although Neville claimed he had the approval of Bourdain’s estate. Bourdain’s widow, Ottavia Busia-Bourdain, disputed the decision, and critics considered the move unethical. It sparked a debate on posthumous vocal privacy.
Identity Rejection: Scarlett Johansson vs. OpenAI “Sky”
In May 2024, actress Scarlett Johansson found herself in a dispute with OpenAI over the launch of “Sky,” a voice option for its chatbot, which was modeled on her voice from the film Her. Johansson said she turned down an offer from OpenAI CEO Sam Altman to license her voice. After the launch, Altman tweeted “Her,” increasing the stakes. OpenAI said, “They hired a different voice actress but retracted the voice amid legal and public scrutiny, showing that even indirect emulation of a public figure’s identity can have serious legal ramifications.”
Protecting the Voice of Authority: Sir David Attenborough
Take, for example, Sir David. This situation is an example of the cultural authority of public figures being exploited through the borrowing of voices, as suggested by Attenborough’s anger at partisan US outlets using his voice without permission. In the name of educational neutrality and objectivity on controversial topics, AI clones have made his personality a tool of party politics.
AI Voice Cloning and Publicity Rights Ruling
Voice cloning is becoming more common, and landmark cases have set the limits of voice protection. Actor Morgan Freeman is suing a documentary company for creating an AI version of his trademark voice without compensation. The court sided with Freeman, deciding that artificial copies of a person’s identity infringe on publicity rights. The landmark ruling has changed the way media companies negotiate with voice actors in Hollywood to require licensing AI voices explicitly.
The Evolution of Public Protections
To prevent further exploitation, individual artists and legal collectives have launched structured defense initiatives:
| Case Category | Core Ethical Violation | Long-Term Legal / Industry Pivot |
|---|---|---|
| Posthumous Media (Bourdain) | Ventriloquizing the deceased without clear familial consensus. | Strict estate management protocols and explicit disclosure requirements for synthetic documentaries. |
| Vocal Emulation (Johansson) | Exploiting a recognizable aesthetic style after direct rejection. | Broadened definitions of identity theft covering intentional “soundalikes.” |
| Political Impersonation (Attenborough) | Spoofing trusted public authorities to amplify propaganda. | Implementation of algorithmic watermarks and real-time deepfake tracking systems. |
RESEARCH AND ARTICLES
Aviatrix. (2026, October 14). Deepfake voice attacks target executive impersonation: your 2026 defense strategy. Aviatrix. Retrieved November 3, 2026, from https://aviatrix.ai/threat-research-center/deepfake-voice-cloning-social-engineering-2026-readiness/
MultiTalk: Scaling Full-Duplex speech models to long, Multi-Party, bilingual conversation. (n.d.). Retrieved November 3, 2026, from https://arxiv.org/html/2609.36903v1
Quoraishee, S. (2026, November 2). How to tell if a voice recording is AI-generated. Scientific American. https://www.scientificamerican.com/article/how-to-tell-if-a-voice-recording-is-ai-generated/
Team, A. (2026, July 10). AI Deepfake Trends: The complete 2025-2026 guide to statistics, threats, detection, and defense strategies for security leaders. Adaptive Security. https://www.adaptivesecurity.com/blog/ai-deepfake-trends-the-complete-2025-2026-guide-to-statistics-threats-detection-and-defense-stra
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