Mimicked Voices and Nonhuman Listening: AI Deepfakes, Speech, and Sonic Manipulation in the Digital War on Ukraine
The essays collected in this series (link to the Introduction) trace how nonhuman listening operates through sound, speech, and platformed media across distinct but interconnected domains. Across these accounts, listening no longer secures meaning or relation; it becomes a site of contestation, where sound is mobilized, processed, and weaponized within systems that privilege circulation, recognition, and response over truth. In this contribution, Olga Zaitseva-Herz examines how nonhuman listening operates under conditions of war, where AI-generated voices and deepfakes destabilize the very grounds of auditory trust. Through the case of Ukraine, she shows how platforms and political actors alike exploit algorithmic listening systems to amplify affect, circulate disinformation, and transform voice into a tool of psychological warfare. Listening, in this context, becomes not a means of understanding but a terrain of uncertainty. –Guest Editor Kathryn Huether
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Russia’s full-scale invasion of Ukraine has unfolded as the most digitally mediated war to date, shaped not only by what circulates online but by how content is heard, interpreted, and amplified. Here, listening is not limited to human hearing: it also includes algorithmic systems that detect, rank, and amplify content, as well as political actors and online publics who interpret and recirculate it. Social media platforms—Telegram, Instagram, TikTok, Facebook—have become sites of psychological warfare where AI-generated audio, video, text, and image-based content are crafted to manipulate perception and provoke rapid emotional responses, often through algorithmic systems attuned to virality and affect. Ukrainian political authorities regularly caution users by saying that everything one reads, hears, or sees could be a psychological weapon. This is not rhetorical. Content is often designed to produce outrage, shock, and despair—emotions that travel quickly across platforms and influence public mood.
AI is used to create fake news videos, synthetic voices, and deepfake conversations, complicating how authenticity is heard and assessed. Some recordings circulating on social media simulate “leaked” phone calls revealing political dissent or strategic plans that are then shared on social media sites such as Telegram, Instagram, and Facebook. At the same time, the fact that people’s original voices can now also be generated with AI means that one can claim that their recorded voice is AI-generated. A widely circulated case involved Russian music producer Iosif Prigozhin, whose alleged call criticizing the Kremlin provoked significant backlash. Soon after he claimed the recording was an AI forgery – a statement whose truth remains unclear, but which strategically exploits growing public awareness of deepfakes as a means of discrediting or distancing from damaging material. Deepfakes thus do not merely deceive; they also destabilize the conditions of listening and trust, turning listening itself into a site of strategic uncertainty.. This uncertainty exploits a growing crisis of trust in listening itself, where voices can always be disavowed as synthetic. Against this backdrop, music and voice emerge as especially powerful media for manipulation, parody, retaliation, and symbolic struggle.
AI Songs as a Tool of Revenge
AI generative tools are also used for irony or parody, such as in the viral remake “Samotni Moskali,” [Lonely Muscovites], which mocks the Ukrainian pop star Ani Lorak, who moved to Russia. On November 13th, 2023, Ukrainian journalist and politician Anton Gerashchenko’s Telegram channel posted a video remake of Ani Lorak’s old song “Poludneva Speka” [Midday Heat], renamed “Samotni Moskali.” This video quickly went viral on social media. Her big hit from the ’00s has been remade into strongly pro-Ukrainian content, featuring clips from current frontlines to illustrate new lyrics generated by an AI voice engineered to closely mimic Lorak’s vocal timbre and affect. The parody relies on listeners recognition of her voice and affective style, while the imitation introduces a strong contentual shift between the original and synthetic lyrics.
This social media burst was a response to Ani Lorak’s claimed political neutrality in the context of Russia´s full-scale war against Ukraine, despite clear signs from her that supported Russia. These actions seemed aimed at revenge and at the same time, the public breakup of her Ukrainian fan base, showing the impact of her choices, while her Ukrainian audience felt betrayed. It led to many satirical memes, including AI-generated songs related to her stage persona, appearing on social media. Knowing that, under current Russian politics, she could get into trouble there if the government took the promoted `support´ for the Ukrainian army seriously. The revenge group went even further by creating a homepage called “Ani Lorak Foundation,” completely dedicated to fundraisers for the Ukrainian army, which is represented like Lorak’s own project where she showcases her support of Ukrainian battalions. Some military drones deployed by the Ukrainian side even ended up bearing stickers with the name of the “Ani Lorak Foundation.“ This case demonstrates how AI tools became instruments of public satire, sabotage and protest in the context of the current full-scale war.
AI Songs as a Weapon
During the full-scale invasion, Russia has been using AI-generated music as a weapon for propaganda and disinformation. In 2023, multiple songs in Ukrainian were created to disrupt Ukraine’s military mobilization efforts and went viral. One of these, the song “Mamo, Ia Ukhyliant” [Mother, I am a Draft Dodger], became particularly popular in a multitude of variations. Their circulation shows how platforms “listen” to wartime content through metrics of repetition, provocation, and affective intensity, amplifying messages not because they are true, but because they are likely to generate reaction and spread. These songs were algorithmically promoted on TikTok and successfully sparked a viral challenge aimed at undermining Ukraine’s mobilization in 2024 by encouraging Ukrainian men to evade the draft, flee, and party abroad instead. In return, Ukrainian intelligence has released an official statement that these songs are products of the Russian disinformation campaign.
This example shows how AI-generated songs are actively used as powerful tools of war, spreading political messages and influencing people’s political choices. Also, the fact that all these songs about draft evasion were released in Ukrainian highlights the goal of targeting Ukrainian men specifically, since Russian men usually don’t speak Ukrainian and therefore wouldn’t be affected by the content. Furthermore, the presence of a large number of these `draft dodger’ songs at the same time created the impression of widespread societal acceptance through repetition and algorithmic amplification. In this way, repetition itself became a signal of apparent legitimacy: the more frequently such content circulated, the more easily platforms and audiences could register it as evidence of broader consensus around draft evasion within Ukrainians.
AI Pictures on Facebook Mimicking Sound and Sonic Affect
Visual disinformation follows similar viral patterns. There has been a surge of AI-generated images with war-related content, often mimicking sound to intensify emotional impact and prompt affective listening by showing a screaming child amid the rubble or a crying soldier in a Ukrainian uniform, paired with a patriotic, pro-Ukrainian message that encourages interaction, such as a like or comment. Even without actual sound, such images solicit a kind of affective listening in which suffering is not literally heard but imagined, projected, and emotionally registered through visual cues. Meanwhile, although this truth-blurring pattern attracted significant attention among many Ukrainians, ironic counter-memes emerged, mocking its primitive approach.
According to warnings from the Ukrainian online security agency, these accounts aim to interact with pro-Ukrainian users, ultimately adding them as friends or followers. Then, when they build a large enough audience, they shift the type of content they share to pro-Russian. The strategy relies on gathering an audience that is specifically pro-Ukrainian, as they interact with images of crying soldiers or the suffering of the Ukrainian people at the front. In this sense, the filtering process functions as a form of nonhuman listening at the level of audience formation: platforms and account managers learn which publics respond to particular emotional cues, cultivate those publics through repeated engagement, and later redirect them toward different ideological content. This creates a filtering mechanism through which an initially pro-Ukrainian audience is gathered, profiled, and later ideologically redirected, alienating loyal followers while pulling political opinion in a more pro-Russian direction.
Pro-Russian AI Songs in Germany to weaken Support of Ukraine
In Germany, AI-generated songs are being utilized as propaganda tools to promote pro-Russian sentiment and anti-Ukrainian views. The right-wing party AfD has embraced AI songs as a potent tool in this regard. Multiple mostly anonymous YouTube accounts have emerged spreading right-wing ideas, with these songs not only addressing German political issues but also openly supporting Russia. For instance, one song titled “Meine Stimme Habt ihr nicht” [You don’t get my vote] features an AI-created avatar of a tall, strong woman holding German and Russian flags. The version of the same song was also released in Russian. The lyrics criticize Germany’s political course, including military aid to Ukraine, and expresses a desire to be friends with Russia. Its circulation across German and Russian suggests that listening is being calibrated for different national and linguistic publics, allowing similar political messages to be heard through distinct affective and ideological frames shaped by language, audience, and context.
Contemporary propaganda is increasingly shaped not just by human intent but by rapidly developing nonhuman listening systems—both in production and amplification. Algorithmic listening and perception are exploited to privilege what provokes, not what is true, complicating efforts to regulate digital hate, emotion, and influence. In this context, listening becomes not only a human practice of interpretation, but also a technical system of detection, ranking, and amplification—and, crucially, a site of failure where truth, trust, and perception can no longer be reliably aligned.
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Featured Image: Photo by Stanislav Vlasov on Unsplash.
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Olga Zaitseva-Herz is an ethnomusicologist working at the intersection of Ukrainian music, war, displacement, and digital culture. She is currently a postdoctoral researcher at the Kule Centre for Ukrainian and Canadian Folklore at the University of Alberta and a guest scholar at Think Space Ukraine at the University of Regensburg. Her research examines how song operates as a medium of political mediation, cultural diplomacy, and historical memory, with a particular focus on popular music and AI-generated sound during Russia’s full-scale invasion of Ukraine. Combining perspectives from ethnomusicology, sound studies, and media analysis, her work investigates how music shapes narratives of resistance, belonging, and global visibility, and how sonic practices illuminate the broader entanglements of culture, technology, and power.
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REWIND! . . .If you liked this post, you may also dig:
Hate & Non-Human Listening, an Introduction–Kathryn Huether
Your Voice is (Not) Your Passport—Michelle Pfeifer
Mapping the Music in Ukraine’s Resistance to the 2022 Russian Invasion—Merje Laiapea
SO! Amplifies: An Interactive Map of Music as Ukrainian Resistance to the 2022 Russian Invasion—Merje Laiapea
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Your Voice is (Not) Your Passport
In summer 2021, sound artist, engineer, musician, and educator Johann Diedrick convened a panel at the intersection of racial bias, listening, and AI technology at Pioneerworks in Brooklyn, NY. Diedrick, 2021 Mozilla Creative Media award recipient and creator of such works as Dark Matters, is currently working on identifying the origins of racial bias in voice interface systems. Dark Matters, according to Squeaky Wheel, “exposes the absence of Black speech in the datasets used to train voice interface systems in consumer artificial intelligence products such as Alexa and Siri. Utilizing 3D modeling, sound, and storytelling, the project challenges our communities to grapple with racism and inequity through speech and the spoken word, and how AI systems underserve Black communities.” And now, he’s working with SO! as guest editor for this series (along with ed-in-chief JS!). It kicked off with Amina Abbas-Nazari’s post, helping us to understand how Speech AI systems operate from a very limiting set of assumptions about the human voice. Last week, Golden Owens took a deep historical dive into the racialized sound of servitude in America and how this impacts Intelligent Virtual Assistants. Today, Michelle Pfeifer explores how some nations are attempting to draw sonic borders, despite the fact that voices are not passports.–JS
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In the 1992 Hollywood film Sneakers, depicting a group of hackers led by Robert Redford performing a heist, one of the central security architectures the group needs to get around is a voice verification system. A computer screen asks for verification by voice and Robert Redford uses a “faked” tape recording that says “Hi, my name is Werner Brandes. My voice is my passport. Verify me.” The hack is successful and Redford can pass through the securely locked door to continue the heist. Looking back at the scene today it is a striking early representation of the phenomenon we now call a “deep fake” but also, to get directly at the topic of this post, the utter ubiquity of voice ID for security purposes in this 30-year-old imagined future.
In 2018, The Intercept reported that Amazon filed a patent to analyze and recognize user’s accents to determine their ethnic origin, raising suspicion that this data could be accessed and used by police and immigration enforcement. While Amazon seemed most interested in using voice data for targeting users for discriminatory advertising, the jump to increasing surveillance seemed frighteningly close, especially because people’s affective and emotional states are already being used for the development of voice profiling and voice prints that expand surveillance and discrimination. For example, voice prints of incarcerated people are collected and extracted to build databases of calls that include the voices of people on the other end of the line.
“Collect Calls From Prison” by Flickr User Cobalt123 (CC BY-NC-SA 2.0)
What strikes me most about these vocal identification and recognition technologies is how their appeal seems to lie, for advertisers, surveillers, and policers alike that voice is an attractive method to access someone’s identity. Supposedly there are less possibilities to evade or obfuscate identification when it is performed via the voice. It “is seen as a solution that makes it nearly impossible for people to hide their feelings or evade their identities.” The voice here works as an identification document, as a passport. While passports can be lost or forged, accent supposedly gives access to the identity of a person that is innate, unchanging, and tied to the body. But passports are not only identification documents. They are also media of mobility, globally unequally distributed, that allow or inhibit movement across borders. States want to know who crosses their borders, who enters and leaves their territory, increasingly so in the name of security.
What, then, when the voice becomes a passport? Voice recognition systems used in asylum administration in the Global North show what is at stake when the voice, and more specifically language and dialect, come to stand in for a person’s official national identity. Several states including Denmark, the Netherlands, the United Kingdom, Switzerland, Sweden, as well as Australia and Canada have been experimenting with establishing the voice, or more precisely language and dialect, to take on the passport’s role of identifying and excluding people.
In the 1990s—not too far from the time of Sneakers release—they started to use a crude form of linguistic analysis, later termed Language Analysis for the Determination of Origin (LADO), as part of the administration of claims to asylum. In cases where people could not provide a form of identity documentation or when those documents would be considered fraudulent or inauthentic, caseworkers would look for this national identity in the languages and dialects of people. LADO analyzes acoustic and phonetic features of recorded speech samples in relation to phonetics, morphology, syntax, and lexicon, as well as intonation and pronunciation.
The problems and assumptions of this linguistic analysis are multiple as pointed out and critiqued by linguists. 1) it falsely ties language to territorial and geopolitical boundaries and assumes that language is intimately tied to a place of origin according to a language ideology that maps linguistic boundaries onto geographical boundaries. Nation-state borders on the African continent and in the Middle East were drawn by colonial powers without considerations of linguistic communities. 2) LADO thinks of language and dialect as static, monoglossic and a stable index of identity. These assumptions produce the idea of a linguistic passport in which language is supposed to function as a form of official state identification that distributes possibilities and impossibilities of movement and mobility. As a result, the voice becomes a passport and it simultaneously functions as a border, by inscribing language into territoriality. As Lawrence Abu Hamdan has written and shown through his sound art work The Freedom of Speech itself, LADO functions to control territory, produce national space, and attempts to establish a correlation between voice and citizenship.
I’ll add that the very idea of a passport has a history rooted in forms of colonial governance and population control and the modern nation-state and territorial borders. The body is intimately tied to the history of passports and biometrics. For example, German colonial administrators in South-West Africa, present day Namibia, and German overseas colony from 1884 to 1919 instituted a pass batch system to control the mobility of Indigenous people, create an exploitable labor force, and institute and reinforce white supremacy and colonial exploitation. Media and Black Studies scholar Simone Browne describes biometrics as “digital epidermalization,” to describe how surveillance becomes inscribed and encoded on the skin. Now, it’s coming for the voice too.
In 2016 the German government took LADO a step further and started to use what they call a voice biometric software that supposedly identifies the place of origin of people who are seeking asylum. Someone’s spoken dialect is supposedly recognized and verified on the basis of speech recordings with an average lengths of 25,7 seconds by a software employed by the German Ministry for Migration and Refugees (in German abbreviated as BAMF). The now used dialect recognition software used by German asylum administrators distinguishes between 4 large Arabic dialect groups: Levantine, Maghreb, Iraqi, Egyptian, and Gulf dialect. Just recently this was expanded with language models for Farsi, Dari and Pashto. There are plans to expand this software usage to other European countries, evidenced by BAMF traveling to other countries to demonstrate their software.
This “branding” of BAMF’s software stands in stark contradiction to its functionality. The software’s error rate is 20 percent. It is based on a speech sample as short as 26 seconds. People are asked to describe pictures while their speech is recorded, the software then indicates a percentage of probability of the spoken dialect and produces a score sheet that could indicate the following: 74% Egyptian, 13% Levantine, 8% Gulf Arabic, 5 % Other. The interpretation of results is left to the caseworkers without clear instructions on how to weigh those percentages against each other. The discretion left to caseworkers makes it more difficult to appeal asylum decisions. According to the Ministry, the results are supposed to give indications and clues about someone’s origin and are not a decision-making tool. However, as I have argued elsewhere, algorithmic or so-called “intelligent” bordering practices assume neutrality and objectivity and thereby conceal forms of discrimination embedded in technologies. In the case of dialect recognition the score sheet’s indicated probabilities produce a seeming objectivity that might sway case-workers in one direction or another. Moreover, the software encodes distinctions between who is deserving of protection and who is not; a feature of asylum and refugee protection regimes critiqued by many working in the field.
The functionality and operations of the software are also intentionally obscured. Research and sound artist Pedro Oliveira addresses the many black-boxed assumptions entering the dialect recognition technology. For instance, in his work Das hätte nicht passieren dürfen he engages with the labor involved in producing sound archives and speech corpora and challenges “ the idea that it might be feasible, for the purposes of biometric assessment, to divorce a sound’s materiality from its constitution as a cultural phenomenon.” Oliveira’s work counters the lack of transparency and accountability of the BAMF software. Information about its functionality is scarce. Freedom of information requests and parliamentary inquiries about the technical and algorithmic properties and training data of the software were denied as the information was classified because “the information can be used to prepare conscious acts of deception in the asylum proceeding and misuse language recognition for manipulation,” the German government argued. While it is not necessarily deepfakes like the one Brandes produced to forego a security system that the German authorities are worried about, the specter of manipulation of the software looms large.
The consequences of the software’s poor functionality can have drastic consequences for asylum decisions. Vice reported in 2018 the story of Hajar, whose name was changed to protect his identity. Hajar’s asylum application in Germany was denied on the basis of a dialect recognition software that supposedly indicated that he was a Turkish speaker and, thus, could not be from the Autonomous Region Kurdistan as he claimed. Hajar who speaks the Kurdish dialect Sorani had been instructed by BAMF to speak into a telephone receiver and describe an image in his first language. The software’s results indicated a 63% probability that Hajar speaks Turkish and the caseworker concluded that Hajar had lied in his asylum hearings about his origin and his reasons to seek asylum in Germany who continued to appeal the asylum decision. The software is not equipped to verify Sorani and should not have been used on Hajar in the first place.
Why the voice? It seems that bureaucrats and caseworkers saw it as a way to identify people with ease and scale language analysis more easily. It is also important to consider the context in which this so-called voice biometry is used. Many people who seek asylum in Germany cannot provide identity documents like passports, birth certificates, or identification cards. This is the case because people cannot take them with them as they flee, they are lost or stolen on people’s journeys, or they are confiscated by traffickers. Many forms of documentation are also not accepted as legitimate by state authorities. Generally, language analysis is used in a hostile political context in which claims to asylum are increasingly treated with suspicion.
The voice as a part of the body was supposed to provide an answer to this administrative problem of states. In response to the long summer of migration in 2015 Germany hired McKinsey to overhaul their administrative processes, save money, accelerate asylum procedures, and make them more “efficient.” In July 2017, the head of the Department for Infrastructure and Information Technology of the German Federal Office for Migration and Refugees hailed the office’s new voice and dialect recognition software as “unrivaled world-wide” in its capacity to determine the region of origin of asylum seekers and to “detect inconsistencies” in narratives about their need for protection. More than identification documents, personal narratives, or other features of the body, the voice, the BAMF expert suggests is the medium that allows for the indisputable verification of migrants’ claims to asylum, ostensibly pinpointing their place of origin.
Voice and dialect recognition technology are established by policy makers and security industries as particularly successful tools to produce authentic evidence about the origin of asylum seekers. Asylum seekers have to sound like being from a region that warrants their claims to asylum: requiring the translation of voices into geographical locations. As a result, automated dialect recognition becomes more valuable than someone’s testimony. In other words, the voice, abstracted into a percentage, becomes the testimony. Here, the software, similarly to other biometric security systems, is framed as more objective, neutral, and efficient way of identifying the country of origin of people as compared to human decision-makers. As the German Migration agency argued in 2017: “The IT supported, automated voice biometric analysis provides an independent, objective and large-scale method for the verification of the indicated origin.”
The use of dialect recognition puts forth an understanding of the voice and language that pinpoints someone’s origin to a certain place, without a doubt and without considering how someone’s movement or history. In this sense, the software inscribes a vision of a sedentary, ahistorical, static, fixed, and abstracted human into its operations. As a result, geographical borders become reinforced and policed as fixed boundaries of territorial sovereignty. This vision of the voice ignores multiple mobilities and (post)colonial histories and reinscribes the borders of nation-states that reproduce racial violence globally. Dialect recognition reproduces precarity for people seeking asylum. As I have shown elsewhere, in the absence of other forms of identification and the presence of generalized suspicion of asylum claims, accent accumulates value while the content of testimony becomes devalued. Asylum applicants are placed in a double bind, simultaneously being incited to speak during asylum procedures and having their testimony scrutinized and placed under general suspicion.
Similar to conventional passports, the linguistic passport also represents a structurally unequal and discriminatory regime that needs to be abolished. The software was framed as providing a technical solution to a political problem that intensifies the violence of borders. We need to shift to pose other questions as well. What do we want to listen to? How could we listen differently? How could we build a world in which nation-states and passports are abolished and the voice is not a passport but can be appreciated in its multiplicity, heteroglossia, and malleability? How do we want to live together on a planet increasingly becoming uninhabitable?
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Featured Image: Voice Print Sample–Image from US NIST
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Michelle Pfeifer is postdoctoral fellow in Artificial Intelligence, Emerging Technologies, and Social Change at Technische Universität Dresden in the Chair of Digital Cultures and Societal Change. Their research is located at the intersections of (digital) media technology, migration and border studies, and gender and sexuality studies and explores the role of media technology in the production of legal and political knowledge amidst struggles over mobility and movement(s) in postcolonial Europe. Michelle is writing a book titled Data on the Move Voice, Algorithms, and Asylum in Digital Borderlands that analyses how state classifications of race, origin, and population are reformulated through the digital policing of constant global displacement.
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REWIND! . . .If you liked this post, you may also dig:
“Hey Google, Talk Like Issa”: Black Voiced Digital Assistants and the Reshaping of Racial Labor–Golden Owens
Beyond the Every Day: Vocal Potential in AI Mediated Communication –Amina Abbas-Nazari
Voice as Ecology: Voice Donation, Materiality, Identity–Steph Ceraso
The Sound of What Becomes Possible: Language Politics and Jesse Chun’s 술래 SULLAE (2020)—Casey Mecija
The Sonic Roots of Surveillance Society: Intimacy, Mobility, and Radio–Kathleen Battles
Acousmatic Surveillance and Big Data–Robin James
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