Explaining the Popularity behind AI Text to Speech Generators

If you have been on TikTok recently, chances are you will have seen an AI-generated text to speech video, often with a deepfake celebrity such as Donald Trump or Morgan Freeman reading out a humorous statement.

These videos, whilst often entertaining, are the result of sophisticated AI technology that is responsible for a seismic shift in how we create and consume content.

But how does this technology actually work, and why is it seemingly blown up in popularity?

How is AI used for text to speech generation?

For centuries, innovators have long tried to create ways of emulating human speech. 19th century inventors such as Joseph Faber developed “speaking machines” such as the Euphonia, which featured a mechanical mouth, tongue and jaws that supposedly mimicked the human throat and vocal organs.

Fast forward a century to the 1950s, the first computer-based speech synthesisers started to develop. In 1961, John Larry Kelly Jr and Louis Gerstman used an IBM 7094 computer to synthesize human speech, resulting in the earliest song sung by a computer - a rather chilling cover of “Daisy Bell (Bicycle Built For Two)”. 

Whilst it might not have had much musical merit, it represented a breakthrough for speech generation, with the original recording being included in the United States National Recording Registry. 

AI text to speech (TTS) generators are built using machine learning models that learn how humans speak. 

These models convert written prompts into a phonetic and prosodic blueprint to determine a sentence structure that can then be transformed into audible speech. 

Natural language processing became a dominant method for text to speech in the 1980s and 90s. NLP enables the TTS generators to better understand the linguistic structure of the prompt, such as the grammar, syntax and semantics. 

This contextual understanding will improve the flow, pronunciation and intonation of the generated speech. It is this technological improvement that enables modern speech generators to sound more natural than the robotic legacy systems. 

The desired qualities of text to speech generators is in how natural the generated speech sounds, and whether the output is intelligible. A high quality generator will output speech that both sounds like human speech and is easy to understand. 

Today’s most advanced text to speech generators use neural networks to deliver the most realistic voices yet generated.

How deepfake audio has developed

Whilst many of the uses of deepfake audio are purely comedic or for satirical purposes, the technology behind it is rather advanced. 

At the core of deepfake text to speech technology is advanced voice cloning. To create an accurate deepfake, typically you will require several minutes or even hours of recorded audio from the target speaker. 

The ideal dataset will include a range of emotions, speaking styles and contexts. Due to the nature of the large dataset required, it is often celebrities known for their speeches, such as US Presidents or famous film stars that are most commonly deepfaked. 

AI text to speech will then extract the acoustic and linguistic features from a speech dataset, including the:

• Phonemes, the distinct units of sound
• Prosody, which refers to the pitch, rhythm and stress
• Voice timbre and intonation

This contextual understanding helps the speech generator to understand how the person speaks, not just what they say. 

Deepfake audio is typically generated using NLP deep neural networks, using models such as Tacotron 2, WaveNet or HiFi-GAN.

If there is a diverse and large enough speech dataset, these cloned voices can be used to create a text to speech system that sounds like the target individual, reading any text input provided, such as a Donald Trump Speech Generator. 

It is these systems that are used so extensively across social media to create entertaining side bites.

Use cases for AI text to speech generators

It isn’t just funny soundboards that use text to speech, the technology has many applications, such as:

Accessibility

Offering accessible options is not just a box-ticking exercise, it is a core part of doing business in 2025. Accessible readers, such as those embedded on websites, help visually impaired users to access digital content.

Education

Not everyone is a visual learner, many learn best through listening, known as auditory learning. AI text to speech generators accommodate students to listen to written content such as textbooks and lecture notes. This is of particular use for language learning. 

Virtual assistants

AI virtual assistants like Alexa and Siri use text to speech to quickly respond to our queries in a natural sounding tone and rhythm. In the US alone, Siri has an estimated 86.5 million users, whilst Alexa has 75.6 million, according to eMarketer.

Custom capabilities with NetGeist

Many businesses are turning towards AI text to speech as a way to engage and interact with their customers. While off-the-shelf text to speech tools can serve general needs, for businesses to fully harness the benefits of the technology, a bespoke option is required. 

That’s where NetGeist comes in.

NetGeist specialise in creating custom NLP solutions such as text to speech systems that match your brand identity and technical requirements. 

A common request we receive is that the AI TTS system must seamlessly integrate with the existing customer relationship manager (CRM) platform. 

Our team provides the expertise required to embed text to speech technology into your workflow. 

From fintech to healthcare, education to entertainment, NetGeist will empower your business to offer smarter, more human experiences to your customers. Contact us to discuss your options.