AI Voice Technology: Its Evolution, Applications, and Impact Across Industries

Table of Contents

AI voice generation technology (also referred to as text-to-speech, voice synthesis, or speech synthesis) uses an interplay of artificial intelligence technologies in order to produce a synthetic voice from text.

After entering mainstream consumer consciousness via the virtual assistant Siri on iOS in 2010, AI voice technology has come a long way. From robotic-sounding synthetic speech, AI voices are becoming more natural-sounding, able to mimic realistic-sounding emotions and vocalizations.

Just as interest in AI art generators has exploded in recent years, interest in AI voice technology has also steadily increased, given the rise of AI software tools readily available to users online. As it continues to evolve in its sophistication, AI voice generation is poised to be a game changer in many industries such as entertainment, content creation, customer service, education, and healthcare.

In this article, we’ll dive deep into AI voice technology: its history, trends, interest, adoption rates, impact across industries, and where the technology itself is headed in the future.

Google Trends comparison of AI voice and AI art generator growth in popularity in the last 4 years (2022-2025).

Before we delve deeper into the evolution of AI voice technology, let’s trace where people’s awareness or curiosity over AI voice technology began, based on Google search results tracked from 2010, following the release of Siri as a standalone iPhone app, all the way up to 2025.

Below is a graph comparing the main search queries related to AI voice: voice synthesis, text-to-speech, and AI voice.

Animated graph comparing the 2010-2025 Google Trends results for voice synthesis, text to speech, and AI voice.

For the search queries, “voice synthesis” and “text to speech”, interest has been quite strong and steady from 2010 to 2025. Its highest peak was in 2023, perhaps owing to the formal release of ElevenLabs’ platform⁠(opens in a new tab or window) in August of that year.

“AI voice” was not quite as popular as a search query in the early 2010s, though there was a bit of a movement in between. Like the two other search queries, interest in AI voice grew bigger in 2023. From thereon, it seems to be the search query of choice for people interested in the technology.

AI voice technology, as we know it today, may trace its origins back to the first computer-based voice synthesis systems of the early 20th century, producing vocoders, reading machines, and calculators for the blind. In recent years, its evolution has accelerated largely due to the development of advanced AI training techniques such as Deep Learning and Natural Language Processing (NLP).

Below is a closer look at the history of AI voice technology and the key milestones that have defined its rapid evolution.

Pre-2015: From Daisy Bell to Siri

The earliest computer-based text-to-speech (TTS) systems were developed at Bell Labs in the 1950s. Computer programmer and physicist, John Kelly built the vocoder, which he used to produce a synthetic voice singing the song, “Daisy Bell”⁠(opens in a new tab or window) in 1961.

In the succeeding decades, voice synthesis technologies continued to develop. From the 60s to the 70s, voice synthesizers started reading and singing in Italian, programmers built synthesizers modeled after the human vocal tract, and reading machines and calculators for the blind were made available to the public via libraries. In the 1980s, synthesized voices began to feature in video games, beginning with Japan’s arcade game Stratovox. Another notable synthetic voice introduced in the 1980s was MacInTalk, demonstrated in the ad for the first Apple Macintosh computer in 1984.

In the 1990s, Microsoft Windows began integrating voice synthesis into its operating systems. This culminated in the introduction of the built-in screen reader, Narrator⁠(opens in a new tab or window), in Windows 2000. The 1990s also saw the diversification of TTS voices, with the development of the first female synthesized voice⁠(opens in a new tab or window) and the expansion to different languages other than English.

While voice synthesis has been in development and application for many years, it was only in the 2010s, with the back-to-back releases of Automatic Speech Recognition (ASR) technologies such as Google Voice Search feature and iOS virtual assistant Siri, that AI voice technology came into mainstream use and attention. Following both breakthroughs, AI as a Service (AIaaS) began to be more popular among the general public. We could also say they’re the mainstream’s first introduction to speech-to-text technology, a subdomain of AI voice technology.

2016-2019: Emotions and nuance in AI voices

The years 2016 to 2017 marked a breakthrough in text-to-speech technology, in particular in the quality of the generated synthetic voices. The breakthrough was ushered in by Google DeepMind's WaveNet⁠(opens in a new tab or window) project, which allowed for the production of more natural-sounding voices. Prior to the introduction of this generative model, TTS systems were mostly based on concatenative TTS, which used a database of short speech fragments recorded from a single speaker and then recombined to form speech.

In 2017, researchers at Google published a paper entitled "Attention is All You Need"⁠(opens in a new tab or window), introducing a landmark in AI technology, the Transformer architecture. This new deep learning architecture revolutionized Natural Language Processing (NLP) and AI voice generation technology in particular, enabling more powerful and efficient models, and forming the basis for Large Language Models (LLM) and further advancement in the domain.

The swift advancements in NLP and other machine learning techniques allowed for AI voices to become more human-like. Using an interplay of different tasks and techniques, NLP helped imbue AI voice⁠(opens in a new tab or window) systems with emotion and nuance.

2020 onwards: The proliferation of generative AI

AI voice generation technologies continued to be adopted in many different industries, in particular in autonomous vehicles, as in-car assistants, and healthcare. Speech-to-text (STT) apps such as Siri and Amazon's Alexa have increasingly become commonplace as regular mobile device features.

However, it was with the introduction of DALL-E 2, the advanced text-to-image generator developed by OpenAI, that interest in generative AI (including AI voice generation) became widespread. Whereas before, the generation of AI voices seemed limited to professionals and corporations, now ordinary users can use readily available AI software to generate different types of content, including highly realistic synthetic voices.

Recent advancements and developments

As generative AI becomes more of an everyday feature of our digital lives, developers continue to build and release increasingly powerful AI voice technologies to the wider public.

2024 saw major movements in AI voice technology. Meta collaborated with high-profile celebrities⁠(opens in a new tab or window) to lend their voices to its AI voice assistant feature. OpenAI released its Advanced Voice Mode⁠(opens in a new tab or window) for paying ChatGPT users, which was then quickly followed by Google's Gemini Live⁠(opens in a new tab or window), positioned as the former's competitor.

ElevenLabs, a startup that quickly grew to become one of the major players in AI voice technology, released highly acclaimed AI voice products, going public with a beta platform in early 2023. These included the ElevenLabs Reader App, the Voice Isolator, and Conversational AI.

However, it has not been without its share of controversies. In 2024, many New Hampshire residents received AI-generated calls (cloning the voice of then-US president Joe Biden) telling them to skip voting. Investigations found that the call was generated⁠(opens in a new tab or window) with ElevenLabs software. The company's library has also been called into question for being trained using several voice actors' voices without their consent.

How is the world using AI voice technology? Let’s take a look at a chart from this 2024 global market analysis report⁠(opens in a new tab or window), showing the top industries with the biggest AI voice generator market shares based on end users.

The three biggest markets for AI voice technology are media and entertainment, customer service and call centers, and education. Advertising and marketing, as well as healthcare, occupy a huge chunk of the market share.

Now let’s take a closer look at the top 5 industries and how each has adopted AI voice and speech synthesis:

Media and entertainment

According to Google Trends, interest in ‘text to speech’ and AI voice technology in the media and entertainment was not significant from 2010 to 2019. It only really surged in 2020, reflecting the mainstream adoption of AI voice technology due to increasing accessibility needs and pandemic-driven demand.

Similarly, the keywords “text to voice” gained popularity as a Google Search term around 2019-2020, coinciding with the rise of content creation platforms. The next few years following the pandemic saw its popularity sustained, as user-generated content became more mainstream. This is largely due to the increased democratization of content creation, thanks to readily available apps that enable creators to produce content with voiceovers, even without traditional voice acting resources.

Even traditionally produced content for film, radio, music, TV, animation, and video games has started to adopt AI voice technology, encompassing text-to-speech, speech-to-text, and voice cloning. Most are using it to streamline processes and automate certain tasks like announcements, closed captioning, dubbing in multiple languages, pitch correction, and more.

With self-publishing on the rise⁠(opens in a new tab or window) comes the equally growing demand for audiobooks and audio narration. To keep costs low for indie authors and increase the accessibility of their books, platforms like Amazon's Virtual Voice⁠(opens in a new tab or window) enable authors to use AI narration to convert their e-books into AI-narrated audiobooks.

Interestingly, the booming industry of podcasting has barely shown any interest in AI voice, reflecting its audience’s demand for emotional authenticity.

Customer service

It’s safe to say that voice assistants such as Siri, Alexa, and Google Assistant helped introduce AI voice technology to the everyday consumer, making the feature ubiquitous. According to this 2025 AI Trends report⁠(opens in a new tab or window), around 20.5% of people worldwide use voice search, with an estimated 8.4 billion voice assistants in use globally.

Another customer service process that can benefit greatly from AI enhancement is the Interactive Voice Response (IVR) system. The traditional automated phone response system has gotten smarter and, in some cases, more accurate and accessible with AI voice technology. Banking⁠(opens in a new tab or window), in particular, is looking to AI voice agents as a more cost-effective way to boost its customer service, scale its operations, and offer services in multiple languages. Other industries with customer service aspects, such as healthcare, hospitality, and education, have also started to integrate AI voice technology into their IVRs.

Education

According to Google Trends, AI voice technology awareness in the education sector began in 2014, as educators began looking into text-to-speech technology as an accessibility and learning tool. Interest in the technology grew steadily, with spikes every 2 to 3 years, until 2020, when the need for accessible digital learning tools grew more urgent as remote learning became part of the new normal.

By 2022, many modern classrooms started adopting text-to-speech technology as part of their assistive tools to support diverse learning needs and multilingual learners. Fast forward to 2025, and text-to-speech technology is now a quintessential part of an inclusive modern classroom, where it’s used for reading comprehension, language learning, and content accessibility.

Generative AI tools, in general, have many uses in the classroom. According to a McKinsey report⁠(opens in a new tab or window), automating certain tasks can potentially save teachers 13 hours of work per week. For instance, instead of having teachers relay updates on their child's academic performance and attendance records, parents can simply retrieve such information via a school’s AI voice-activated IVR system.

Another task teachers can do more efficiently with AI voice technology is creating engaging and personalized learning materials. Curriculum developers and e-learning professionals can also do the same with AI voice. One such working example is what Khan Academy is doing with its AI tutor, Khanmigo. The AI tool is programmed so that it can learn a student's specific strengths, weaknesses, and existing knowledge base, helping it deliver personalized instruction. It has since added a text-to-speech component⁠(opens in a new tab or window), allowing the learner to hear the AI tutor’s answers in their pre-selected AI voice and language.

Healthcare

AI voice technology is poised to play a crucial role in the healthcare industry post-pandemic, with health systems all over the world at an inflection point⁠(opens in a new tab or window).

One of the industry’s biggest challenges that AI voice technology can help with is staff shortage and burnout. For example, AI voice-to-text transcription services⁠(opens in a new tab or window) can potentially save doctors up to 17% and registered nurses up to 51% of their work time, typically spent on administrative documentation. Staff shortages typically affect the efficiency of routine patient interactions. With AI voice-activated IVRs, clinics and hospitals can now automate tasks like appointment scheduling, delivering lab results, and sending medication reminders.

Aside from supporting staff with routine and administrative tasks, AI voice technology also has the potential to extend patient support and triage. Case in point: the Rwanda AI Triage chatbots⁠(opens in a new tab or window). These AI-powered chatbots, interacting via voice or text, are designed to gather information about a caller’s symptoms and use this information to provide recommendations for appropriate care or response. If implemented, these triage chatbots could streamline the overburdened triage process in many countries, especially those that have yet to recover from the brunt of the pandemic.

AI also holds great promise for actual medical applications. In this instance, voice cloning, a highly contested domain of AI voice technology, can be used to help individuals with speech impairments⁠(opens in a new tab or window), either with a chosen synthetic voice or a synthesized version of their own voice.

Advertising and marketing

Based on Google Trends data, the advertising and marketing industry began to express interest in AI voice in 2022, coinciding with the growing popularity of generative AI among mainstream users.

The applications of AI voice in the advertising and marketing sphere are closely related to its primary uses in media and entertainment. It has largely been used to streamline or automate certain processes such as telemarketing, closed captions, and auto-translating subtitles. AI voiceover has also found its way into short-form digital advertising, empowering smaller players to scale their advertising and marketing campaigns even with limited budgets.

Some brands, however, have started incorporating AI voice technology in much more creative ways. One example is Oreo’s marketing campaign, Say It with Oreo⁠(opens in a new tab or window), which incorporates the AI voice cloning of a popular Bollywood actor’s voice. A user visits the campaign’s microsite, enters a question regarding an awkward situation, which is then fed into an LLM API, triggering a response. This response is then fed into an AI voice API, which synthesizes the text into the actor’s cloned voice AI.

Content creation is a growing industry with a global market size⁠(opens in a new tab or window) valued at USD 32.28 billion in 2024, estimated to grow at a CAGR of 13.9% from 2025 to 2030. With increasing demand for all types of content, creators are looking to generative AI (including AI voice technology) as a means to scale their production and increase engagement, while keeping their costs manageable and their quality up to par with consumer expectations.

The business case for AI voices

One of the biggest potential benefits of AI voice technology on content creation is the increase in time and cost efficiency. Voiceover production with a human talent can take days and can cost $100–$500+ per hour (not counting the additional cost and turnaround time for revisions and other corrections). With ElevanLabs, Murf.AI⁠(opens in a new tab or window), and other AI voice platforms, generating and revising AI voiceovers can take minutes for the cost of a monthly subscription, which is roughly the same price as an hour at the studio.

AI voice can also significantly augment productivity in content creators. A study by the University of British Columbia (UBC) on TikTok creators⁠(opens in a new tab or window) reveals that the adoption of AI voice technology has increased video production by 21.8%, particularly among less experienced creators, who have seen a rise in engagement with their AI-assisted content.

AI voice technology also offers creators a way to scale their content production to cater to a wider audience. Platforms like HeyGen can convert a single script into several multilingual videos voiced by AI, removing the need for manual translation and re-recording. One successful example of using AI voice for localization is MotorVision Group's collaboration with DubFormer⁠(opens in a new tab or window). The venture began with subtitling the channel's content in Greek and Brazilian Portuguese and then grew into successfully produced episodes with voiceovers in Latin American Spanish, resulting in a 17% reduction in total localization costs.

Finally, with AI voice technology, having a consistent brand voice⁠(opens in a new tab or window) is much more achievable. With more consumers interacting virtually with brands via AI voice agents and digital assistants embedded in cars and smart devices, AI voices offer a more sustainable and cost-effective way to stay on-brand while keeping up with the demand for high-quality branded audio.

Voice characteristics that drive engagement

Although AI voice still has a long way to go, it has made significant improvements in recent years, especially when compared to the robotic voices we typically associate with AI. In this section, we'll look at voice characteristics that affect user experience with AI voices.

Perceived tone, gender, and age in voice

A study about voice assistants⁠(opens in a new tab or window) found that users are likely to be more persuaded to make purchase decisions by VA voices that have positive or neutral tones, mimicking middle-aged male or younger female voices. This shows that perceived tone, gender, and age can affect a user's trust, ultimately influencing how they act on a certain call to action.

Cuteness⁠(opens in a new tab or window) may also help with engagement, but it depends on the target audience and the context of the interaction.

Prosody modulation

Another study on AI voice assistants⁠(opens in a new tab or window) revealed that users are more likely to engage with an AI voice that can modulate its pitch, speed, and rhythm. Adjusting these elements based on the context of any given conversation makes conversing with AI voices feel more natural and hence more engaging.

Giving users the ability to personalize the AI voice⁠(opens in a new tab or window) they're engaging with adds more comfort to the interaction, leading to a better experience.

Empathy and responsiveness

Certain advancements in AI voice, such as Hume.ai⁠(opens in a new tab or window) and ChatGPT's advanced voice mode component⁠(opens in a new tab or window), are trained to recognize and respond to user emotions. They're designed to foster a sense of empathy, which is meant to lead to better engagement. However, even their creators have warned⁠(opens in a new tab or window) that these highly advanced AI voices may encourage emotional dependency among users.

Cultural and demographic considerations

Audience demographics (gender, age, cultural background) also influence how users interact with AI voices.

Sometimes, the experience is similar across different groups, with only subtle differences. For instance, an age-centric study⁠(opens in a new tab or window) showed that both young and older adults share a similar skepticism toward AI voice assistants. In a gender-based study comparing neural and standard text-to-speech⁠(opens in a new tab or window), males and females tend to perceive neural TTS (deep learning models) as significantly less trustworthy than human speech. Meanwhile, standard TTS (pre-recorded speech segments or signal processing models) is perceived by males (but not females) as less trustworthy.

Aside from sharing a similar level of distrust toward neural TTS, binary males and females tend to share the same preference toward gendered technology, according to a Columbia Business School research⁠(opens in a new tab or window). However, there's a fear that this only reinforces gender stereotypes and bias and exclusion of the nonbinary community. The introduction of Project Q⁠(opens in a new tab or window), the first genderless voice assistant, was meant to address this concern. However, at the moment, it has yet to be developed⁠(opens in a new tab or window) into a fully functioning AI voice.

The experience of native and non-native English speakers using the same AI voice technology tends to vary in terms of overall satisfaction. For instance, in this study of voice assistants in Thailand⁠(opens in a new tab or window), both native and non-native English speakers report finding the same VAs usable. However, the overall satisfaction is much lower for non-native speakers who express frustration over the limited non-English language support and having to repeat certain commands.

This shows that even given the leaps AI voice technology has taken toward user engagement, there's still a lot of work to be done in terms of representation.

Creator success stories

As more and more creative professionals embrace the use of generative AI⁠(opens in a new tab or window) to streamline processes, enhance creativity, and scale production, these content creators are leading the charge, centering AI technology and using AI voice tools in their content.

Sinead Bovell

Sinead Bovell (@sineadbovell on TikTok) is a prominent futurist and tech commentator with over 284K TikTok followers. She regularly discusses and demonstrates AI voice technologies in her content.

Gianluco Mauro

Gianluco Mauro (@gianluca.mauro on TikTok) is an author and entrepreneur with over 172K TikTok followers. He runs AI Academy and uses his TikTok page to create and promote content that showcases practical applications of AI voice tools.

Krish Naik

Krish Naik's YouTube Channel covers various AI technologies, including voice synthesis, in his educational content. The AI educator and machine learning pioneer is the founder of KrishAI Technologies and leverages his extensive experience in AI to make the topic accessible to his audience. At the moment, his channel has amassed 1.1 million YouTube subscribers.

Allie K. Miller

Allie K. Miller (@alliekmiller on X, TikTok, Instagram, and AKMofficial on YouTube) has a total of 1.5M followers across all her creator pages. She's a renowned AI advisor and investor, and she covers machine learning in different industries and reviews new AI tools on her platforms.

Factnomenal

The faceless YouTube channel has over 824K subscribers. It uses an AI voice to narrate its educational content on science, history, and other types of fascinating content.

Imogen Heap

The singer-songwriter developed an AI assistant called "Mogen"⁠(opens in a new tab or window) which replicates her voice for music production. She trains the AI voice tool with Plaud Note, a ChatGPT-powered voice recorder. She has recently released a set of AI style filters through the AI music platform Jen⁠(opens in a new tab or window), which users can use as a basis for new AI song generations for $4.99 per style filter. Many creators are also using AI video translator⁠(opens in a new tab or window) tools to reach global audiences by automatically translating their content into multiple languages.

The unprecedented advancement in AI voice technology raises several ethical concerns, particularly as it becomes increasingly accessible.

Reinforced bias

One such concern is the linguistic bias that popular AI voice models seem to reinforce. Popular AI voice platforms with low-quality options for non-American and British AI voices (specifically African, Australian, and Indian English accents) are seen to support existing linguistic hierarchies⁠(opens in a new tab or window) and encourage digital exclusion. Speech-to-text services used by Amazon, IBM, Google, Microsoft, and Apple have an error rate of 35 percent for words spoken by Black speakers⁠(opens in a new tab or window), leading to reinforced misrepresentation.

Gender bias is also prevalent in AI voice technology. One study on VoxCeleb⁠(opens in a new tab or window) (a dataset consisting of short YouTube clips featuring human speech) found that female speakers have an error rate of 49.35% greater than male speakers.

Personal and national security concerns

Voice clones are also another growing source of concern. Realistic-sounding deepfakes can and have been used maliciously for financial fraud⁠(opens in a new tab or window), disinformation campaigns⁠(opens in a new tab or window), and cyberbullying⁠(opens in a new tab or window). Such threats to personal and national security have become more common as the technologies grow increasingly democratized, making stricter technology governance and platform control a much more urgent need.

Intellectual property and copyright infringement

Voice cloning has also been perceived as a potential threat to the livelihoods and reputations of public figures, such as actors and singers, who rely on their voices professionally. With some AI models trained on copyrighted materials without permission, the call for legislative changes is becoming increasingly loud and more urgent.

Voice actor perspectives

As AI voice generation grows more sophisticated in its nuance and quality, a big concern is how this will pose a threat to voice actors’ jobs and likenesses. While big organizations such as the Screen Actors Guild—American Federation of Television and Radio Artists (SAG-AFTRA) have taken steps to protect their members from unethical AI practices⁠(opens in a new tab or window), voice actors for small voice acting jobs, like background work, are not covered by the organization.

For now, the immediate recommendation is for voice actors to watch their contracts and negotiate for the fair use of their voices⁠(opens in a new tab or window), including how their voices will be used, compensation for voice cloning outside of the original performance, and for how long their voices are under contract with a particular company

Global efforts to govern AI voice

Some progress has been made to govern AI technology, including AI voice, in certain parts of the world.

So far, the European Union has the world’s first (and so far only) comprehensive AI law⁠(opens in a new tab or window). In the US, there’s the proposed NO FAKES Act / NO AI FRAUD Act⁠(opens in a new tab or window), which aims to give individuals the right to control the use of their voice and visual likeness in AI-generated replicas. In China, a landmark case ruled⁠(opens in a new tab or window) in favor of a voiceover artist whose voice was replicated and used in audiobooks without her consent. The ruling reinforced the country’s new Civil Code, which protects voice rights under portrait rights, reminding corporations to obtain legal rights for any AI initiatives.

Many of the current legislative changes are flawed at best, especially given the rapid evolution of the technology. The biggest challenge, therefore, is how to create future-proof regulations that proactively anticipate the potential harms and misuse of the technology.

Generative AI, including AI voice technology and its many subsectors, is set to transform multiple industries in the years to come.

AI voice recognition, arguably the biggest sector of AI voice technology, is projected to expand to USD 44.7 billion by 2034⁠(opens in a new tab or window). Its biggest segment, speech recognition, holds exciting potential for the healthcare, finance, automotive, and customer service industries.

AI voice generators, on the other hand, are projected to reach a market value of USD 21.75 billion by 2030⁠(opens in a new tab or window), as voice cloning and generation continue to evolve and be adopted in the media and entertainment sectors.

What is driving this unprecedented growth? For starters, the core technologies behind generative AI and AI voice are rapidly developing, allowing speech recognition to be more accurate and AI voice generators to generate more authentic-sounding synthetic voices. One crucial step needed for AI voice is the development of more systems that support non-English languages and dialects, which can potentially expand the market in untapped regions.

The clamor for a highly personalized user experience is also instrumental in the evolution of AI voice technology. As the user base for Internet of Things (IoT) devices, smart home automation systems, and advanced automotive applications (i.e., in-car assistants and self-driving vehicles) continues to expand, so does the need for even more intuitive AI voice recognition systems. People have now gotten accustomed to conversational AI⁠(opens in a new tab or window) through ChatGPT’s advanced voice mode, so virtual assistants and smart devices will need to adapt to meet user expectations. In the near future, built-in voice recognition systems that can understand a user’s command and have a clear speaking voice won’t be enough; a natural, self-aware, and conversational AI voice system will be the standard.

Implementing AI voice into your content strategy requires careful consideration. Below, we explore some of the most popular platform options and what they offer so you can decide which might best suit your needs.

Top 5 AI voice platforms

Here’s a look at the top 5 AI voice platforms and their surge in popularity in the last five years, based on Google search trends.

AI voice generators started trending back in May 2020 with Play AI, but their popularity only surged in January 2023, coinciding with the release of ElevenLabs' beta platform. Following this rise in interest, other AI voice generators like Murf AI, Lovo AI, and Fliki AI entered the scene. Though not as popular as the top 2 AI voice generators, the latter 3 do attract a steady number of searches.

Let's look at each AI voice platform briefly.

1. ElevenLabs

Arguably the most popular AI voice platform online, ElevenLabs has a massive library of over 5,000 voices across 32 languages. It's known for its powerful voice cloning capabilities. Still, one of its top features is its impressive AI dubbing, which can preserve the emotional nuance and timing of the original speaker.

2. Play AI

Play AI has a library of over 300 voices across more than 30 languages. While it offers other AI voice products, it is most known for its natural-sounding conversational AI voice agents.

3. Murf AI

Murf AI has a smaller library of voices, but its customization features are highly intuitive. One of its top-rated features is its API integration, which allows users to integrate the voice system into different tools and workflow systems, including Canva, Adobe, and Notion.

4. Lovo AI

Lovo AI started as an AI voice platform but has since grown to become an all-in-one AI content creation assistant. Along with its text-to-speech, speech-to-text, and voice cloning capabilities, it also has its own AI video generator, AI writer, and AI art generator.

5. Fliki AI

Fliki AI is primarily a text-to-video generator with AI voiceover capabilities. It takes your text and generates a basic video out of it, allowing you to choose an AI voice to narrate your video.

All platforms offer a basic free account with limited credits that replenish regularly, as well as paid plans for different kinds of users.

Implementing AI voice technology into your content strategy

Let's look at different types of content you can include in your strategy and how AI voice technology can help scale your creation.

AI voiceover for videos

Even if you don't have the budget for professional voiceover or recording studies, AI voice generation can help you provide professional-sounding narration for educational and training videos, product demo videos, and social media content. You can also enhance your videos with custom AI sound effects⁠(opens in a new tab or window) to create a more immersive experience.

Podcast and audiobook automation

Podcasting is expected to grow exponentially in the coming years⁠(opens in a new tab or window) and presents a great opportunity for creators and brand owners looking to diversify their content. If you've got an existing blog, text-to-speech platforms can help you turn your articles into podcast episodes, increasing your content's accessibility.

You can also use the same tools to turn your books, courses, and training material into instant audiobooks.

Content localization

With AI voice dubbing, you can automatically translate your content into multiple languages and expand your content's reach.

Accessibility for your visually-impaired audience

Automatically employing text-to-speech to convert any written content into audio is a great way to make your content accessible to audiences with visual impairments.

The rapid advancements in AI voice technology in the last five years have begun to transform many facets of people’s lives, and this is just the start. As with all advancements, the benefits come with trade-offs, and it’s up to us to create regulations that will allow everyone to harness the full potential of AI voice in a way that won’t cause any harm, but instead augment human creativity and contribute to better accessibility for all.

If you’re looking to incorporate AI voice generation into your brand or content strategy, consider Canva’s AI voice generator and enjoy seamless integration into your workflow.

Discover all the ways AI can empower your creativity and productivity with Canva AI⁠(opens in a new tab or window).