Text To Speech Technology: How Voice Computing is Making the World More Accessible

Text to Speech Technology

In a world where new technologies are emerging at an exponential rate and our daily lives are being mediated by speakers and sound waves, text-to-speech technology is the latest force evolving in the way we communicate.

Text-to-speech technology in the field of computer science enables the conversion of speech text into audio speech. Also known as voice computing, text-to-speech (TTS) often consists of a database of recorded human speech to train the computer to generate sound waves similar to the natural sound of human speech. This process is called speech synthesis.

The technology is trailblazing and happens on a large scale regularly in the field. Popular tools that introduce text-to-speech technology into our daily lives include artificial intelligence-enabled virtual assistants such as Amazon's Alexa and Google Assistant.

In addition to converting language text into speech, these virtual assistants use speech recognition software to absorb sound waves generated by human speech, extract meaning from that audio data, and respond to synthetic voices. In its most advanced form, text-to-speech technology enables artificial intelligence to communicate with humans.

With the advent of interactive voice advertising, text-to-speech technology has been used for advertising purposes, proving to drive brand recalls rather than the adjacent types of advertising. Here are the 10 best pieces of text-to-speech software you need to know. TTS can also be an optimal tool for converting vast masses of text into playable audio data.

Learn how speech technology works, what role human voices play in creating synthetic voice, and how text is used for ease of speaking and listening everywhere.

How do voice computing and text to speech technology work?

Artificial Intelligence

At the basic level, the way to send a text to Speech Technology functions is as follows:

First, the text on the speech engine hears the sound waves produced by the human voice and converts them into language data. This process is called automatic speech recognition (ASR). However, the data needs to make sense of those words before doing anything. This is known as the natural language generation (NLG) process.

Artificial intelligence has developed the ability to come up with original, creative responses to the audio data it takes. As James Vlahos, author of Talk to Me: How Voice Computing Works, Lives, Works and Thinks Our Lives, explains, “The neural network is what makes computers so basic. They’re not just capturing the written word, human speech-movie subtitles, and Reddit threads and doing so after extensive training. They are learning the style of how people communicate and what people might say after person A. ”

Once the text is created on the speech engine that it wants to convert into speech, it needs to create the sound needed for the articulation. This stage of the process involves converting language characters into phonemes or different sounds. To achieve this, the text-to-speech engine must understand the context of the sentence to determine the appropriate tense.

Using Human Voice for a Synthetic One

Using Human Voice for a Synthetic One

One of the leading models of speech synthesis is called concatenative Text to Speech, where "a very large database of small speech fragments is recorded from a single speaker and then reassembled to form complete utterances."

Famous reference points for voice computing in the 2001 film include a sensitive computer from HAL: A Space Odyssey and a speech synthesizer used by Stephen Hawking, but the future synthetic sound is not entirely robotic. The sound of authentic human speech will play an important role in creating the original synthetic voices in a human-like growing voice.

If you’re creating a synthetic voice for your brand, by inputting the voices of real artists, you have the opportunity to put it into your brand voice personality, or verbal identity. As text-to-speech technology becomes more widespread, we are choosing a race, gender, and other vocal features of the voice that allow us to create a unique synthetic voice that represents who we represent.

How Text to Speech Technology Contributes to Increased Accessibility

Among the various abilities, text to speech is used as an assistive technology to help make the world easier when it comes to the way you speak and listen. Here are some of the main ways text-to-speech technology is used:

Text to speech as an assistant for people with learning disabilities

While you're publishing written material for a wider audience, one of the tricks is to use text to speech technology to make it more accessible to those dealing with a particular type of learning disability.

More than 750 million young people and adults worldwide lack reading skills or are illiterate, and between 15 and 20% of the world's population have language-based learning disabilities. Dyslexia is the most common of these.

Even for your audience members who can understand a piece of your content, reading everything comfortably can still be a problem. Giving your audience the option to read any piece of your content aloud makes it easier to reach people with a large literacy level.

Text to speech for learning a new language

An estimated 255 million people worldwide have some degree of visual impairment, of which one million are blind. Text-to-speech technology allows those who are unable to read from the screen to access written content by listening to it.

If one does not have a visual impairment, reading for a long period can still cause visual strain. In such a situation, text-to-speech technology is a valuable tool that allows the reader to re-open without looking at the screen without pausing on their investment with the textual content.

Text to speech enabling consumption on the go

Text-to-speech technology allows customers to listen to any text on the go or while multitasking. Studies have shown that we spend more time than we plug into audio sources: from listening to music and podcasts to relying on smart speakers to deliver news and instructive audio content such as recipe lists or weather reports, in which we handle surrounding tasks.

Most people can’t find enough time to read in their day. Text-to-speech technology converts words that the reader would otherwise have to focus on from the heart, converting them into sounds that can bring them along wherever they go.

Text-to-speech technology is also beneficial because it does not require standing on a microphone to record long streams of text, especially when the audio content needs to be given with little or no warning. The technology is ideal for converting news briefings or regularly updated airing courses - like micro clearing, which is the training of content sent into smaller content - from text to automated speech.

In addition to being optimal for frequently updated content, text-to-speech technology is also suitable for long-form content. This can include words from books, articles, training documents, or any piece of writing. Text to speech can allow anyone to use any content anywhere, even when the listener is engaged in complementary activities.

Text to speech for people with medical conditions that affect their voice

Text technology can help send voice for people who have a speech impairment or have a medical condition that affects their ability to speak.

ALS, Parkinson's, stroke, and brain injury are associated with acquired disorders in nearly one in ten people in the United States. Acquired speech impairment may include decreased ability to speak as a whole.

For most people, their voice is as familiar as their own, as different as their fingerprints. In recent years, new forms of text-to-speech technology have been developed that can reproduce a person's voice even before it is diagnosed.

Groundbreaking initiatives such as Project Euphonia, developed by artificial intelligence company Deep Mind in collaboration with Google, are working hard to "synthesize high-quality, natural-sounding voices using minimally recorded speech data."

After football player Tim Shaw diagnoses ALS, he lost the ability to speak. Still, using the show's NFL audio recordings, DeepMind and Google's AI team were able to recreate the football player's previous voice. The results are taken in this short documentary:

In the documentary, Google AI Product Manager Julie Cattiau outlines that Project Euhponia’s two primary goals are “to improve speech recognition for people who have a variety of medical conditions,” as well as “to give people their voice back, this means recreating the way they used to sound before they were diagnosed.”

In the documentary, Google AI Product Manager Julie Cattiau adapted that Project Euphonia's two primary objectives were to "improve speech recognition for people with a variety of medical conditions" as well as "give people back their voice, which means recreating the way they used to sound before they were diagnosed.”

Summary

Train custom voice models using your audio recordings to create a unique and more natural-sounding sound for your organization. You can define and select voice profiles that suit your organization and quickly adjust to changes in voice needs without recording new phrases. TTS converts text into audio speech.