“Hey Google, Talk Like Issa”: Black Voiced Digital Assistants and the Reshaping of Racial Labor
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. Today Golden Owens explored what happens when companies sell Black voices along with their Intelligent Virtual Assistants. Tune in for a deep historical dive into the racialized sound of servitude in America. Even though corporations aren’t trying to hear this absolutely critical information–or Black users in general–they better listen up. –JS
In October 2019, Google released an ad for their Google Assistant (GA), an intelligent virtual assistant (IVA) that initially debuted in 2016. As revealed by onscreen text and the video’s caption, the ad’s announced that the GA would soon have a new celebrity voice. The ten-second promotion includes a soundbite from this unseen celebrity—who states: “You can still call me your Google Assistant. Now I just sound extra fly”— followed by audio of the speaker’s laughter, a white screen, the GA logo, and a written question: “Can you guess who it is?”
Consumers quickly speculated about the person behind the voice, with many posting their guesses on Reddit. The earliest comments named Tiffany Haddish, Lizzo, and Issa Rae as prospects, with other users affirming these guesses. These women were considered the most popular contenders: two articles written about the new GA voice cited the Reddit post, with one calling these women Redditors’ most popular guesses and the other naming only them as users’ desired choices. Those who guessed Rae were proven correct. One day after the ad, Google released a longer promo revealing her as the GA’s new voice, including footage of Rae recording responses for the assistant. The ad ends with Rae repeating the “extra fly” line from the initial promo, smiling into the camera.
Google’s addition of Rae as an IVA voice option is one of several recent examples of Black people’s voices employed in this manner. Importantly, this trend toward Black-voiced IVAs deviates from the pre-established standard of these digital aides. While there are many voice options available, the default voices for IVAs are white female voices with flat dialects. This shift toward Black American voices is notable not only because of conversations about inclusion—with some Black users saying they feel more represented by these new voices—but because this influx of Black voices marks a spiritual return to the historical employment of Black people as service-providing, labor-performing entities in the United States, thus subliminally reinforcing historical biases about Black people as uniquely suited for performing this type of work.
Marketed as labor-saving devices, IVAs are programmed to assist with cooking and grocery shopping, transmit messages and reminders, and provide entertainment, among other tasks. Since the late 2010s they have also been able to operate other technologies within users’ homes: Alexa, for example, can control Roomba robotic vacuums; IVA-compatible smart plugs or smart home devices enable IVAs to control lights, locks, thermostats, and other such apparatuses. Behaviorally, IVAs are designed and expected to be on-call at all times, but not to speak or act out of turn—with programmers often directed to ensure these aides are relatable, reliable, trustworthy, and unobtrusive.
Far from operating in a vacuum, IVAs eerily evoke the presence of and parameters set for enslaved workers and domestic servants in the U.S.—many of whom have historically been Black American women. Like IVAs, Black women servants cooked, cleaned, entertained children, and otherwise served their (predominantly white) employers, themselves operating as labor-saving devices through their performance of these labors. Employers similarly expected these women to be ever-available, occupy specific areas of the home, and obey all requests and demands—and were unsettled if not infuriated when maids did not behave according to their expectations.
White women being the default voices of IVAs has somewhat obfuscated the degree to which these aides have re-embodied and replaced the Black servants who once predominantly executed this work, but incorporating Black voices into these roles removes this veil, symbolically re-implementing Black people as labor-performing entities by having them operate as the virtual assistants who now perform much of the labor Black workers historically performed. Enabling Black people to be used as IVAs thus re-aligns Black beings with the performance of service and labor.
While Black women were far from the only demographic conscripted into domestic labor, by the 1920s they comprised a “permanent pool of servants” throughout the country, due largely to the egress of white American and immigrant women from domestic service into fields that excluded Black women (183). Black women’s prominence in domestic service was heavily reflected in early U.S. media, which overwhelmingly portrayed domestic servants not just as Black women, but as Black Mammies—domestic servant archetypes originally created to promote the myth that Black women “were contented, even happy as slaves.” Characters like Gone with the Wind’s “Mammy” pulled both from then-current associations of Black women with domestic labor and from white nostalgia for the Antebellum era, and specifically for the archetypal Mammy—marking Black women as idealized labor-performing domestics operating in service of white employers. These on-screen servants were “always around when the boss needed them…[and] always ready to lend a helping hand when times were tough” (36). Historian Donald Bogle dubbed this era of Hollywood the “Age of the Negro Servant,” referenced in this reel from the New York Times.
Cinema and television merely built from years of audible racism on the radio—America’s most prominent form of in-home entertainment in the first half of the 20th century—where Black actors also played largely servant and maid roles that demanded they speak in “distorted dialect, exaggerated intonation, rhythmic speech cadences, and particular musical instruments” in order to appear at all (143). This white-contrived portrayal of Black people is known as “Blackvoice,” and essentially functions as “the minstrel show boiled down to pure aurality” (14). These performances allowed familiar ideals of and narratives about Blackness to be communicated and recirculated on a national scale, even without the presence of Black bodies. Labor-performing Black characters like Beulah, Molasses and January, Aunt Jemima, and Amos and Andy were prominent in the Golden Age of Radio, all initially voiced by white actors. In fact, Aunt Jemima’s print advertising was just as dependent on stereotypical representations of her voice as it was on visual “Mammy” imagery.
When Black actors broke through white exclusion on the airwaves, many took over roles once voiced by white men and/or were forced by white radio producers and scriptwriters to “‘talk as white people believed Negroes talked’” so that white audiences could discern them as Black (371). This continuous realignment undoubtedly informs contemporary ideas of labor, labor performance, and laboring bodies, further promoted by the sudden influx of Black voice assistants in 2019.
Specifically, these similarities demonstrate that contemporary IVAs are intrinsically haunted by Black women slaves and servants: built in accordance with and thus inevitably evoking these laborers in their positioning, programming, and task performance. Further facilitating this alignment is the fact that advertisements for Black-voiced IVAs purposefully link well-known Black bodies in conjunction with their Black voices. Excepting Apple’s Black-sounding voice options for Siri, all of the Black IVA voice options since 2019 have belonged to prominent Black American celebrities. Prior to Issa Rae, GA users could employ John Legend as their digital aide (April 2019 until March 2020). Samuel L. Jackson became the first celebrity voice option for Amazon’s Alexa in December 2019, followed by Shaquille O’Neal in July 2021.
The ads for Black-voiced IVAs thus link these disembodied aides not just to Black bodies, but to specific Black bodies as a sales tactic—bodies which signify particular images and embodiments of Blackness. The Samuel L. Jackson Alexa ad utilizes close-ups of Jackson recording lines for the IVA and of Echo speakers with Jackson’s voice emitting from them in response to users. John Legend is physically absent from the ad announcing him as the GA; however, his celebrity wife directs the GA to sing for her instead, after which she states that it is “just like the real John”—thus linking Legend’s body to the GA even without his onscreen presence. Amazon has even explicitly explored the connection between the Black-voiced IVA and the Black body, releasing a 2021 commercial called “Alexa’s Body” that saw Alexa voiced and physically embodied by Michael B. Jordan—with the main character in the commercial insinuating that he is the ideal vessel for Alexa.
By aligning these bodies with, and having them act as, labor-performing devices in service of consumers, these advertisements both re-align Blackness with labor and illuminate how these devices were always already haunted by laboring Black bodies—and especially, given the demographics of the bodies who most performed the types of labors IVAs now execute, laboring Black women’s bodies. That the majority of the Black celebrities employed as Black IVA voices are men suggests some awareness of and attempt to distance from this history and implicit haunting—an effort which itself exposes and illuminates the degree to which this haunting exists.
In some cases, the Black people lending their voices to these IVAs also speak in a way that sonically suggests Blackness: Issa Rae’s “Now I’m just extra fly,” for example, incorporates Black American slang through the use of the word “fly. As part of African American Vernacular English (AAVE), the term “fly” dates back to the 1970s and denotes coolness, attractiveness, and fashionableness. Because of its inclusion in Hip Hop, which has become the dominant music genre in the United States, the term, its meaning, and its racial origins are widely known amongst consumers. By using the word “fly,” Rae nods not only at these qualities but also at her own Blackness in a manner that is recognizable to a mainstream American audience. Due in part to Hip Hop’s popularity, U.S.-based media outlets, corporations, and individuals of varying races and ethnicities regularly appropriate AAVE and Black slang terms, often without regard for the culture that created them or the vernacular they stem from. The ad preceding Issa Rae’s revelation as the GA specifically invited users to align the voice with a celebrity body, and users’ predominant claims that the voice was a Black woman’s suggest that something about the voice conjured Blackness and the Black female body.
This racial marking was also likely facilitated by how people naturally listen and respond to voices. As Nina Sun Eidsheim notes in The Race of Sound, “voices heard are ultimately identified, recognized, and named by listeners at large. In hearing a voice, one also brings forth a series of assumptions about the nature of voice” (12). This series of assumptions, Eidsheim asserts in “The Voice as Action,” is inflected by the “multisensory context” surrounding a given voice, i.e., “a composite of visual, textural, discursive, and other kinds of information” (9). While we imagine our impressions of voices as uniquely meaningful, “we cannot but perceive [them] through filters generated by our own preconceptions” (10). As a result, listening is never a neutral or truly objective practice.
For many consumers, these filters are informed by what Jennifer Lynn Stoever terms the sonic color line, “a socially constructed boundary that racially codes sonic phenomena such as vocal timbre, accents, and musical tones” (11). Where the racial color line allows white people to separate themselves from Black people on the basis of visual and behavioral differences, the sonic color line allows people “to construct and discern racial identities based on voices, sounds, and particular soundscapes” and to assign nonwhite voices with “differential cultural, social, and political value” (11). In the U.S., the sonic color line operates in tandem with the American listening ear, which “normalizes the aural tastes and standards of white elite masculinity as the singular way to interpret sonic information” (13) and therefore marks-as-Other not only the voices and bodies of Black people, but also those of non-males and the non-elite.
Ironically, the very listening practices which make consumers register particular voices and vocal qualities as Black also make Black voices inaccessible to Alexa and other IVAs. Scholarship on Automated Speech Recognition (ASR) systems and Speech AI observes that many Black users find it necessary to code-switch when speaking to IVAs, as the devices fail to comprehend their linguistic specificities. A study by Christina N. Harrington et al. in which Black elders used the Google Home to seek health information discovered that “participants felt that Google Home struggles to understand their communication style (e.g., diction or accent) and language (e.g., dialect) specifically due to the device being based on Standard English” (15). To address these struggles, participants switched to Standard American English (SAE), eliminating informal contractions and changing their tone and verbiage so that the GA would understand them. As one of the study’s participants states,
You do have to change your words. Yes. You do have to change your diction and yes, you have to use… It cannot be an exotic name or a name that’s out of the Caucasian round. …You have to be very clear with the English language. No ebonic (15).
This incomprehension extends to Black Americans of all ages, and to other IVAs. A study by Allison Koenecke et al. on ASR systems produced by Amazon, Google, IBM, Microsoft and Apple discovered that these entities had a harder time accurately transcribing Black speech than white speech, producing “an average word error rate (WER) of 0.35 for black speakers compared with 0.19 for white speakers.” (7684). A study by Zion Mengesha et al. on the impact of these errors on Black Americans—which included participants from different regions with a range of ages, genders, socioeconomic backgrounds and education-levels—discovered that many felt frustrated and othered by these mistakes, and felt further pressure to code-switch so that they would not be misunderstood. Koenecke et al. concluded that ASR systems could not understand the “phonological, phonetic, or prosodic characteristics of” AAVE (7687), and that this ignorance would make the use of these technologies more difficult for Black users—a sentiment that was echoed by participants in the study conducted by Mengesha et al., most of whom marked the technology as working better for white and/or SAE speakers (5).
The speech recognition errors these technologies demonstrate—which also extend to speakers in other racial and ethnic groups—illuminate the reality that despite including Black voices as IVAs, these assistant technologies are not truly built for Black people, or for any person that does not speak Standard American English. And where AAVE is largely associated with Blackness, SAE is predominantly associated with whiteness: as a dialect widely perceived to be “lacking any distinctly regional, ethnic, or socioeconomic characteristics,” it is recognized as being “spoken by the majority group or the socially advantaged group” in the United States—both groups which are solely or primarily composed of white people. SAE is so identified with whiteness that Black people who only speak Standard English are often told that they sound and/or “talk” white, and Black people who deliberately invoke SAE in professional and/or interracial settings (i.e., code switching) are described as “talking white” or using their “white voice” when doing so. That IVAs and other ASR systems have such trouble understanding AAVE and other non-standard English dialects suggests that these technologies were not designed to understand any dialect other than SAE—and thus, given SAE’s strong identification with whiteness, were designed specifically to assist, understand, and speak to white users.
Writing on this phenomena as a woman with a non-standard accent, Sinduja Rangarajan highlights in “Hey Siri—Why Don’t You Understand More People Like Me?” that none of the IVAs currently on the market offer any American dialect that is not SAE. And while users can change their IVA’s accents, they are limited to Standard American, British, Irish, Australian, Indian, and South African—which Rangarajan rightly highlights as revealing who the IVAs think they are talking to, rather than who their user actually is. That most of these accents belong to Western, predominantly white countries (or to countries once colonized by white imperialists) strongly suggests that these devices are programmed to speak to—and perform labor for—white consumers specifically.
When considering the primary imagined and target users of IVAs, the sudden influx of Black-voiced IVAs becomes particularly insidious. Though they may indeed make some Black users feel more represented, cultivating this representation is merely a byproduct of their actual purpose. Because these technologies are not built for Black consumers, Black-voiced IVAs are meant to appeal not to Black users, but to white ones. Rae, Jackson, and the other Black celebrity voices may provide a much-needed variety in the types of voices applied to IVAs, but they primarily operate as “further examples of technology companies using Black voices to entertain white consumers while ignoring Black consumers.” Black-voiced assistants, after all, no better understand Black vernacular English than any of the other voice options for IVAs, a reality marking Black speech patterns as enjoyable but not legitimate.
By excluding Black consumers, the companies behind these IVAs insinuate that Blackness is only acceptable and worthy of consideration when operating in service of whiteness. Where Black people as consumers have been delegitimized and disregarded, Black voices as labor-saving assistants have been welcomed and deemed profitable—a reality which further emphasizes how historical constructions of Black people as labor-performing devices haunts these contemporary technologies. Tech companies reinforce historical positionings of white people as ideal consumers and Black people as consumable products—repeating historical demarcations of Blackness and whiteness in the present.
In imagining the futures of IVAs, the companies behind them would need to reconsider how they interact—or fail to interact—with Black users. Both Samuel L. Jackson and Shaquille O’Neal, the last of the Black-celebrity-voiced IVAs still currently available to users, will be removed as Alexa voice options by September 2023, presenting an opportunity for these companies to divest. Whether or not the brands behind these IVAs take this initiative, consumers themselves can be critical of how AI technologies continue to reestablish hierarchical systems, of their own interactions with these devices, and of who these technologies are truly made for. In being critical, we can perhaps begin to envision alternative, reparative modes of AI technology—modes that serve and support more than one kind of user.
Featured Image: Issa Rae gif from the 2017 Golden Globes
Golden Marie Owens is a PhD candidate in the Screen Cultures program at Northwestern University. Her research interests include representations of race and gender in American media and popular culture, artificial intelligence, and racialized sounds. Her doctoral dissertation, “Mechanical Maids: Digital Assistants, Domestic Spaces, and the Spectre(s) of Black Women’s
Labor,” examines how intelligent virtual assistants such as Apple’s Siri and Amazon’s Alexa evoke and are haunted by Black women slaves, servants, and houseworkers in the United States. In her time at Northwestern, she has had internal fellowships through the Office of Fellowships and the Alice Kaplan Institute for the Humanities. She currently holds an MMUF Dissertation Grant through the Institute for Citizens and Scholars and Ford Dissertation Fellowship through the National Academy for Sciences, Engineering, and Medicine.
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Mr. and Mrs. Talking Machine: The Euphonia, the Phonograph, and the Gendering of Nineteenth Century Mechanical Speech – J. Martin Vest
Echo and the Chorus of Female Machines—AO Roberts
Black Excellence on the Airwaves: Nora Holt and the American Negro Artist Program —Chelsea Daniel and Samantha Ege
Spaces of Sounds: The Peoples of the African Diaspora and Protest in the United States–Vanessa Valdes
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Beyond the Every Day: Vocal Potential in AI Mediated Communication
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 for Sounding Out! (along with ed-in-chief JS!). It starts today, with Amina Abbas-Nazari, helping us to understand how Speech AI systems operate from a very limiting set of assumptions about the human voice– are we training it, or is it actually training us?
Hi, good morning. I’m calling in from Bangalore, India.” I’m talking on speakerphone to a man with an obvious Indian accent. He pauses. “Now I have enabled the accent translation,” he says. It’s the same person, but he sounds completely different: loud and slightly nasal, impossible to distinguish from the accents of my friends in Brooklyn.The AI startup erasing call center worker accents: is it fighting bias – or perpetuating it? (Wilfred Chan, 24 August 2022)
This telephone interaction was recounted in The Guardian reporting on a Silicon Valley tech start-up called Sanas. The company provides AI enabled technology for real-time voice modification for call centre workers voices to sound more “Western”. The company describes this venture as a solution to improve communication between typically American callers and call centre workers, who might be based in countries such as Philippines and India. Meanwhile, research has found that major companies’ AI interactive speech systems exhibit considerable racial imbalance when trying to recognise Black voices compared to white speakers. As a result, in the hopes of being better heard and understood, Google smart speaker users with regional or ethnic American accents relay that they find themselves contorting their mouths to imitate Midwestern American accents.
These instances describe racial biases present in voice interactions with AI enabled and mediated communication systems, whereby sounding ‘Western’ entitles one to more efficient communication, better usability, or increased access to services. This is not a problem specific to AI though. Linguistics researcher John Baugh, writing in 2002, describes how linguistic profiling is known to have resulted in housing being denied to people of colour in the US via telephone interactions. Jennifer Stoever‘s The Sonic Color Line (2016) presents a cultural and political history of the racialized body and how it both informed and was informed by emergent sound technologies. AI mediated communication repeats and reinforces biases that pre-exist the technology itself, but also helping it become even more widely pervasive.
Mozilla’s commendable Common Voice project aims to ‘teach machines how real people speak’ by building an open source, multi-language dataset of voices to improve usability for non-Western speaking or sounding voices. But singer and musicologist, Nina Sun Eidsheim describes how ’a specific voice’s sonic potentiality [in] its execution can exceed imagination’ (7), and voices as having ‘an infinity of unrealised manifestations’ (8) in The Race of Sound (2019). Eidsheim’s sentiments describe a vocal potential, through musicality, that exists beyond ideas of accents and dialects, and vocal markers of categorised identity. As a practicing vocal performer, I recognise and resonate with Eidsheim’s ideas I have a particular interest in extended and experimental vocality, especially gained through my time singing with Musarc Choir and working with artist Fani Parali. In these instances, I have experienced the pleasurable challenge of being asked to vocalise the mythical, animal, imagined, alien and otherworldly edges of the sonic sphere, to explore complex relations between bodies, ecologies, space and time, illuminated through vocal expression.
Following from Eidsheim, and through my own vocal practice, I believe AI’s prerequisite of voices as “fixed, extractable, and measurable ‘sound object[s]’ located within the body” is over-simplistic and reductive. Voices, within systems of AI, are made to seem only as computable delineations of person, personality and identity, constrained to standardised stereotypes. By highlighting vocal potential, I offer a unique critique of the way voices are currently comprehended in AI recognition systems. When we appreciate the voice beyond the homogenous, we give it authority and autonomy, ultimately leading to a fuller understanding of the voice and its sounding capabilities.
My current PhD research, Speculative Voicing, applies thinking about the voice from a musical perspective to the sound and sounding of voices in artificially intelligent conversational systems. Herby the voice becomes an instrument of the body to explore its sonic materiality, vocal potential and extremities of expression, rather than being comprehended in conjunction to vocal markers of identity aligning to categories of race, gender, age, etc. In turn, this opens space for the voice to be understood as a shapeshifting, morphing and malleable entity, with immense sounding potential beyond what might be considered ordinary or everyday speech. Over the long term this provides discussion of how experimenting with vocal potential may illuminate more diverse perspectives about our sense of self and being in relation to vocal sounding.
Vocal and movement artist Elaine Mitchener exhibits the disillusion of the voice as ‘fixed’ perfectly in her performance of Christian Marclay’s No!, which I attended one hot summer’s evening at the London Contemporary Music Festival in 2022. Marclay’s graphic score uses cut outs from comic book strips to direct the performer to vocalise a myriad of ‘No”s.
Mitchener’s rendering of the piece involved the cooperation and coordination of her entire body, carefully crafting lips, teeth, tongue, muscles and ligaments to construct each iteration of ‘No.’ Each transmutation of Mitchener’s ‘No’s’ came with a distinct meaning, context, and significance, contained within the vocalisation of this one simple syllable. Every utterance explored a new vocal potential, enabled by her body alone. In the context of AI mediated communication, we can see this way of working with the voice renders the idea of the voice as ‘fixed’ as redundant. Mitchener’s vocal potential demonstrates that voices can and do exist beyond AI’s prescribed comprehension of vocal sounding.
In order to further understand how AI transcribes understandings of voice onto notions of identity, and vocal potential, I produced the practice project Polyphonic Embodiment(s) as part of my PhD research, in collaboration with Nestor Pestana, with AI development by Sitraka Rakotoniaina. The AI we created for this project is based upon a speech-to-face recognition AI that aims to be able to tell what your face looks like from the sound of your voice. The prospective impact of this AI is deeply unsettling, as its intended applications are wide-ranging – from entertainment to security, and as previously described AI recognition systems are inherently biased.
This multi-modal form of comprehending voice is also a hot topic of research being conducted by major research institutions including Oxford University and Massachusetts Institute of Technology. We wanted to explore this AI recognition programme in conjunction with an understanding of vocal potential and the voice as a sonic material shaped by the body. As the project title suggests, the work invites people to consider the multi-dimensional nature of voice and vocal identity from an embodied standpoint. Additionally, it calls for contemplation of the relationships between voice and identity, and individuals having multiple or evolving versions of identity. The collaboration with the custom-made AI software creates a feedback loop to reflect on how peoples’ vocal sounding is “seen” by AI, to contest the way voices are currently heard, comprehended and utilised by AI, and indeed the AI industry.
The video documentation for this project shows ‘facial’ images produced by the voice-to-face recognition AI, when activated by my voice, modified with simple DIY voice devices. Each new voice variation, created by each device, produces a different outputted face image. Some images perhaps resemble my face? (e.g. Device #8) some might be considered more masculine? (e.g. Device #10) and some are just disconcerting (e.g. Device #4). The speculative nature of Polyphonic Embodiment(s) is not to suggest that people should modify their voices in interaction with AI communication systems. Rather the simple devices work with bodily architecture and exaggerate its materiality, considering it as a flexible instrument to explore vocal potential. In turn this sheds light on the normative assumptions contained within AI’s readings of voice and its relationships to facial image and identity construction.
Through this artistic, practice-led research I hope to evolve and augment discussion around how the sounding of voices is comprehended by different disciplines of research. Taking a standpoint from music and design practice, I believe this can contest ways of working in the realms of AI mediated communication and shape the ways we understand notions of (vocal) identity: as complex, fluid, malleable, and ultimately not reducible to Western logics of sounding.
Featured Image: Still image from Polyphonic Embodiments, courtesy of author.
Amina Abbas-Nazari is a practicing speculative designer, researcher, and vocal performer. Amina has researched the voice in conjunction with emerging technology, through practice, since 2008 and is now completing a PhD in the School of Communication at the Royal College of Art, focusing on the sound and sounding of voices in artificially intelligent conversational systems. She has presented her work at the London Design Festival, Design Museum, Barbican Centre, V&A, Milan Furniture Fair, Venice Architecture Biennial, Critical Media Lab, Switzerland, Litost Gallery, Prague and Harvard University, America. She has performed internationally with choirs and regularly collaborates with artists as an experimental vocalist
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What is a Voice?–Alexis Deighton MacIntyre
Voice as Ecology: Voice Donation, Materiality, Identity–-Steph Ceraso
Mr. and Mrs. Talking Machine: The Euphonia, the Phonograph, and the Gendering of Nineteenth Century Mechanical Speech – J. Martin Vest
One Scream is All it Takes: Voice Activated Personal Safety, Audio Surveillance, and Gender Violence—María Edurne Zuazu
Echo and the Chorus of Female Machines—AO Roberts
On Sound and Pleasure: Meditations on the Human Voice– Yvon Bonefant