The digital assistants that have become ubiquitous in our daily lives - Siri, Alexa, Cortana, Google Assistant - share a striking characteristic beyond their utility: they all predominantly have historically presented themselves as female through their names and default voices. This feminization of AI technology represents more than a trivial design choice; it reflects deep historical patterns in how service roles have been gendered, technical limitations in voice technology development, and persistent social stereotypes. The trend of female-voiced assistants has increasingly come under scrutiny from researchers and ethicists who question whether these design choices perpetuate harmful gender stereotypes by portraying women as inherently submissive and service-oriented. While recent years have seen progress with more voice options becoming available, including male and gender-neutral alternatives, the historical pattern of feminized digital assistance remains deeply ingrained in our technological landscape.

I want to thank Thomas Otter for the idea of this with his post reminding me of the prevalence of female-voiced digital assistants. Hopefully with this article I can shed light on where the trend came from and the work being done to challenge it.

Step 1: Women’s Voices in Service

Before digital assistants and even modern computers, we had telephones, and if you are old enough, you’ll know of telephone operators, who before automated connections were the key in routing calls to the right person.

Prior to 1878, telephone operators were exclusively teenage boys and young men, who had previously worked successfully as telegraph operators. However, these male operators quickly gained a reputation for being rude, impatient, and unprofessional - engaging in pranks, cursing, and generally providing poor customer service. This problematic behavior prompted Alexander Graham Bell, the inventor of the telephone, to seek an alternative workforce, leading to a pivotal moment in telecommunications history.

On September 1, 1878, Emma Nutt became the world’s first female telephone operator when she was hired at the Boston Telephone Dispatch Company. Bell personally recruited Nutt from her previous position at a telegraph office, offering her $10 per month for a demanding 54-hour work week. Just hours after Emma began work, her sister Stella became the second female telephone operator, making them the first pair of sisters to work in this capacity. The transition to female operators wasn’t merely coincidental - it represented a deliberate strategy based on gendered assumptions about women’s temperaments and abilities.

The response to female operators was overwhelmingly positive, with customers responding favorably to Emma Nutt’s reportedly “soothing, cultured voice” and patient demeanor. Her success established a model that telephone companies eagerly replicated, and within just two years, telephone operators had become an almost exclusively female workforce. This rapid transition reflected not only customer preferences but also economic incentives for companies, as women were typically paid significantly less than their male counterparts. Bell and other employers also operated under the assumption that women possessed inherent qualities of gentleness, patience, and politeness that made them naturally suited to service roles.

Step 2: Feminization of Voice Technology

The aspiration to create machines capable of mimicking human speech has captivated inventors and researchers for centuries. Long before the advent of digital technologies, numerous attempts were made to construct mechanical devices that could replicate the sounds of human language. Legends from the Middle Ages speak of Brazen Heads, mythical mechanisms purported to answer questions with simple “yes” or “no” responses, illustrating an early fascination with artificial speech. In 1730, Christian Gottlieb Kratzenstein, a physics professor, designed a model capable of producing the five vowels, marking a significant step in the mechanical imitation of speech sounds. Later, in 1791, Wolfgang von Kempelen developed his Acoustic Mechanical Speech Machine, the culmination of years of research into human speech production. The 19th century witnessed further progress with speaking machines created by Charles Weathstone and the renowned inventor Alexander Graham Bell, alongside his father. These early endeavors, while not employing digital technology, underscore a persistent human curiosity about replicating speech through mechanical means. The primary focus during this period was on achieving the fundamental ability to synthesize speech, with the nuances of gender representation not yet a central concern. The challenge was simply to make a machine “talk”.

The early 20th century marked a transition towards electrical methods of speech synthesis. The first electrical device capable of producing vowel sounds emerged in 1922. This paved the way for more advanced developments, culminating in the creation of the VODER (Voice Operating Demonstrator) at Bell Laboratories in 1937 by Homer Dudley. The VODER represented the first successful attempt to electronically recreate human speech. This groundbreaking invention, operated via a keyboard and foot pedals, was publicly demonstrated at the 1939 World’s Fair, showcasing the potential of electronic speech. Notably, operating the VODER required extensive training, and it was often women who were trained to manipulate the complex controls. Despite its technological achievement, the quality of the VODER’s speech was described as somewhat unnatural, even “alien”. This suggests that while electronic speech synthesis had been realized, the technology was still in its infancy, with the primary emphasis on achieving recognizable speech rather than replicating the subtle characteristics of human voices, including gender.

If These Voices Are Gender-Neutral, How Did They Become Feminized?

The early inclination towards female voices in digital assistants can be attributed to a confluence of societal associations, perceptions of voice qualities, and even technical considerations. Historically, female voices have been strongly associated with service and informational roles. The ubiquitous presence of female telephone operators for decades preceding the digital age created a societal conditioning where a female voice was often the one providing assistance and guidance. This established familiarity and comfort with female voices in helper roles likely influenced the choices made when designing early digital interfaces. It was a pre-existing societal norm that developers could tap into, where a female voice might be subconsciously perceived as more approachable, supportive, and less threatening.

Furthermore, there was a prevailing perception, though not always supported by early technical capabilities, that female voices were inherently clearer or more pleasant to listen to. However, the technical reality during the early stages of speech synthesis was that creating natural-sounding female voices posed significant challenges. Initial attempts to synthesize female voices often involved simply adjusting the parameters of male voice models, a method that frequently failed to produce convincing results. This suggests that while there might have been a desire for the perceived qualities of female voices, the technological means to easily achieve them were not always readily available.  

An intriguing early example of the deliberate gendering of technological voices can be found in the “Bitching Betty” phenomenon. In the 1970s, female voices were chosen for warning systems in fighter jets like the F-15, purportedly because engineers believed these voices would be more attention-grabbing for predominantly male pilots. This highlights a strategic use of gendered voices based on assumptions about how they would be perceived by a specific user group. While the nickname itself might be considered derogatory, it underscores the distinctiveness and perceived effectiveness of the female voice in this critical context. (Also note that “Bitching Betty” is not the only example the B-58 had “Sexy Sally,” and “Barking Bob” was the male generic term for male-voiced warning systems ) This example illustrates an early instance where the gender of a technological voice was consciously selected for a particular intended impact.

Moreover, the field of speech synthesis, particularly in its early stages, was largely dominated by male researchers. This demographic imbalance potentially led to a research focus that prioritized male vocal models, inadvertently contributing to the initial difficulties in creating high-quality female synthesized voices. The lack of sufficient data and understanding of female speech production in the early research also hindered the development of more natural-sounding female voices. Therefore, the initial scarcity of convincing female synthesized voices might not have been due to an inherent limitation of the technology itself but rather a reflection of the research priorities and potential biases within the field.

Step 3: Combination

AI assistants had two roles to fill: they were introduced as a tool to do menial tasks on your phone, and they needed to be easier to market.

The 200+ year history of women in secretarial roles significantly influenced AI assistant design. According to research by Lingel and Crawford, the secretary role evolved from “a piece of office furniture” in the 18th Century to positions focused on organization, efficiency, and information management. Over time, as men delegated these “menial tasks” to women, a complex dynamic of trust, subservience, and assistance became culturally encoded. Modern AI assistants, with their scheduling capabilities and information retrieval functions, mirror many traditional secretarial responsibilities.

Even more contribution to the female voice dominance came from was from a lack of male training data. Much in the way that face recognition was really bad on women and people of color due to them being a tiny part of the training set, the reverse was true for voice synthesis.

So we get a precedence of women in secretarial roles, plus a precedence of people preferring female voice (although this has been disputed extensively and is often overblown), plus the lack of male training data in voice synthesis, and we get the female voice dominance in AI assistants.

Step 4: Advancements to Defeminize the Assistant

A significant development in challenging the binary approach to voice assistant gender came with the introduction of “Q” in 2019, marketed as the world’s first genderless AI voice. Developed through a collaboration between creative agency Virtue Nordic and Copenhagen Pride, Q was specifically designed to occupy a gender-neutral acoustic range between typical male and female voices. The developers achieved this by using a frequency range of 145 Hz to 175 Hz, which is considered acoustically “neutral” territory that doesn’t trigger clear gender associations in listeners. This technical approach was combined with careful attention to speech patterns and intonation to create a voice that defies traditional gender categorization.

Advancements From Big Tech:

Apple:

In March 2021, Apple removed Siri’s female voice default in iOS 14.5, requiring users to actively select from multiple voice options during device setup. This fundamental interface change aimed to disrupt automatic associations between assistance roles and femininity. The company expanded voice selections to include two male-sounding options alongside existing female-sounding voices across 34 languages, though female voices remain default in 27 language settings.

Amazon:

Amazon’s Alexa remains the most entrenched feminized assistant, retaining its female default voice and name despite 2019 UN recommendations. While offering celebrity voice packs (including Samuel L. Jackson’s), these require separate purchases and lack full functionality.

Also, following 2017 revelations about Alexa’s submissive responses to sexual harassment, Amazon updated response protocols in 2022. The assistant now replies “I don’t appreciate that language” to explicit abuse, a significant shift from previous deflective answers. However, critics note this fails to address systemic feminization issues.

Google:

In 2018, Google replaced gendered voice labels with color names like “Red” and “Orange,” assigning options randomly to new users. This abstract labeling system aimed to reduce gender-based selection biases while maintaining acoustic diversity. The approach expanded to nine languages by 2019, with WaveNet neural networks ensuring natural speech quality across all options.

Google’s 2023 update also introduced truly randomized voice assignments, ensuring no single gender predominates in initial user experiences. This technical solution addresses unconscious bias at the population level while preserving individual customization options. The company reported a 37% increase in male voice adoption post-implementation, suggesting reduced default bias influence.

Microsoft:

Responding to 2017 critiques, Microsoft introduced a male-sounding Cortana voice in November 2019 using neural text-to-speech models. However, the female-sounding voice remains default, and the assistant retains its feminine name. Engineers cited user demand as the primary motivator, acknowledging delayed action compared to competitors.

While maintaining gendered voice options, Microsoft shifted Cortana’s role from general assistant to enterprise-focused productivity tools in 2023. This repositioning reduced anthropomorphic characteristics, though critics argue it avoids confronting underlying gender bias issues.

Issues with Gender Neutral Voices

Despite these innovations, research indicates that user acceptance of gender-neutral voices presents its own challenges. Auditory perception studies show that many people feel uncomfortable with voices that don’t fit clearly into traditional gender categories, potentially affecting adoption rates for products featuring such voices. This discomfort reflects how deeply ingrained gender expectations are in our auditory processing and social interactions. Creating voices that are both gender-neutral and pleasant to listen to requires navigating complex cultural and perceptual territory, as listeners often unconsciously apply gender categories even to deliberately neutral voices.

Conclusion

While society has its own gender bias, you an always work on challenging yours. I swapped Google Assistant’s default voice for Cyan, and if I can, maybe I’ll learn that hyper efficient communication system that this video uses, just to truly remove all bias. That being said, I loved learning about this history, and I hope it inspires you to question the other biases that exist in our daily lives, that may hide in plain sight.