Text-to-speech technology has improved the way many businesses handle routine tasks, train employees, and perform data entry. Modern text-to-speech tools can enhance the programs companies use every day, though their usefulness extends well beyond business applications.
The evolution of text-to-speech capabilities is contributing to a more accessible world. Tools that incorporate text to speech are helping people with dyslexia and other learning disabilities that affect reading ability. Web-based content like that in online degree programs is becoming more accessible for people who have limited vision or other limitations that affect reading text on screens.
People who need to convert text to speech typically access the technology through a website, app, or program. These platforms utilize text-to-speech engines for conversion.
Developers use various text-to-speech tools to when creating programs and apps. These tools translate files of any size, from small passages to entire books and websites.
Google Cloud Text-to-Speech is an application programming interface (API) that developers can use to add text-to-speech functionality to websites, applications, and programs. Google's WaveNet technology powers the API. Companies access WaveNet for tasks like enhancing automated customer service systems or adding interactive features to mobile devices.
Similar to Google Cloud Text-to-Speech, Amazon Polly is a cloud-based text-to-speech API. Developers primarily use this service to create speech-enabled apps. Popular apps like Duolingo, Bandwidth, and GoAnimate use Amazon Polly for text-to-speech functions.
Amazon Polly features include:
This Chrome and Firefox extension uses text-to-speech technology to create audio files from webpage text. The extension gives users access to both Amazon and Google text-to-speech services. Read Aloud users can convert text from almost any website category from the currently loaded webpage.
Read Aloud features include:
Dozens of other extensions and apps allow access to Amazon Polly and Google WaveNet voices.
Each of these tools features natural-sounding voices, unlike older tech that relied on more robotic voices. The transition to natural voices has introduced a new era of text-to-speech technology. The goal is to make it easier to produce, realistic, natural sounding speech from any text, in any language, as quickly as possible, and as cheaply as possible.
Today, voice output can sound real enough for organizations to incorporate text to speech into various business functions. Companies use text to speech for automated phone systems, online and phone-based customer service, healthcare tasks, and in many other capacities.
Text-to-speech tools utilize a process called speech synthesis in an attempt to create human, realistic text to speech. Early speech synthesis was quite limited. Even as late as the early 2000s, programs that utilized speech synthesis could only generate a single computer-generated voice. Users were unable to change aspects like pitch or speed.
The speech synthesis tools of today are robust. The technology is fast, and the output quality output is adaptable to the purpose at hand. Perhaps most strikingly, much of today's output is text to speech with emotion, an improvement over the more monotonous, robotic voices from years past.
Industry insiders predict that voice quality will continue to improve over the next several years. Eventually, text-to-speech output may become indistinguishable from live human voices.
Either Google or Amazon powers the majority of text-to-speech platforms used today. These similar yet distinct providers use different methods for speech synthesis.
Google WaveNet uses “deep learning neural network algorithms to synthesize text into a variety of voices and languages,” according to the WaveNet website. Google's speech synthesis experts have spent years researching machine learning techniques and applying that knowledge to WaveNet. The result is speech so natural that Google claims to have reduced the computer-generated versus human performance gap by 70 percent.
WaveNet allows Google's Text-to-Speech API to accept either raw text or SSML-formatted data. Raw text, sometimes referred to as plain text, is entered directly by end users.
SSML, or speech synthesis markup language, is a part of the programming of the API. It is an intermediary step between raw data and what is sent to the API for conversion. Developers use tools built into their platforms and operating systems to decode SSML data, a process that is seamless and invisible to end users. SSML allows for more nuance and manipulation of voice outputs.
Amazon Polly is similar to Google WaveNet in that it can convert both plain text and SSML into natural text-to-speech translations. However, the output quality is generally considered less robust versus WaveNet's output.
Developers can access and control Amazon Polly via an API, language-specific software development kits (SDKs), the AWS Management Console, and the AWS command-line interface (CLI).
Both services claim to offer the latest and greatest in text to speech technology. Amazon Polly released a “newscaster” voice and neural text-to-speech capabilities in 2019. Google WaveNet quickly followed up with its own announcement that it could now convert into almost 200 voices and a dozen additional languages. No doubt, the trend will continue—Amazon and Google appear to be continually competing for new text to speech users.
End users have more options than ever for accessing high-tech text-to-speech services, though that access often comes at a premium. An affordable text-to-speech tool like Easy Text to Speech allows more users to utilize modern text-to-speech technology. We offera basic unlimited service with free text-to-speech conversions and a low-cost option for enhanced, natural-sounding output. Our service is based on Google's text to speech platform.. No account or subscription is required. Please email email@example.com with questions, comments or suggestions.