📚 What is TTS(Text-to-Speech)?

Text-to-Speech, also termed TTS, is a form of supportive technology that brings ease and comfort in life. The system reads out digital texts out loud and clear enough for a person to understand. TTS is also known as read-aloud technology, widely accepted for its flexibility. It is a single touch away, where the website’s text converts into audio.

The system expands across all devices such as smartphones, laptops, desktops, and tablets, considered ideal for children, the public aged above 20, and people with disabilities. The struggle of reading and stressing eyes towards electronic devices are all gone with TTS while increasing focus, learning, and the habit of reading online through listening. So if you are a blogger, reader, or website owner, TTS is software that will expand your horizon of knowledge. But what are the benefits of having a voice for everything, no limitation, and no boundary? It is segregated according to the users as they are the person to use the services.

Allowing people to converse with machines is a long-standing dream of human-computer interaction. The ability of computers to understand natural speech has been revolutionised in the last few years by the application of deep neural networks (e.g., Google Voice Search). However, generating speech with computers — a process usually referred to as speech synthesis or text-to-speech (TTS) — is still largely based on so-called concatenative TTS, where a very large database of short speech fragments are recorded from a single speaker and then recombined to form complete utterances. This makes it difficult to modify the voice (for example switching to a different speaker, or altering the emphasis or emotion of their speech) without recording a whole new database.

📚 How Does TTS Technology Work?

The TTS process involves several stages:

• 1. Text Input: The first step is to input the text that you want to convert into speech. This can be a written document, a webpage, a chatbot conversation, or even a social media post.
• 2. Text Analysis: The text is then analyzed to determine the correct pronunciation, intonation, and rhythm. This involves identifying the individual words, phrases, and sentences, as well as the context in which they are used.
• 3. Speech Synthesis: The analyzed text is then processed using speech synthesis algorithms to generate the corresponding audio output. This involves creating a digital representation of the spoken words, including the pitch, tone, and volume.
• 4. Audio Output: The final step is to produce the audio output, which can be played through speakers, headphones, or other audio devices.

📚 Types of TTS Technology

There are several types of TTS technology, including:

• Rule-Based Systems: These systems use pre-defined rules to generate speech. They are simple and efficient but may not produce high-quality speech.
• Statistical Models: These systems use statistical models to generate speech. They are more advanced than rule-based systems and can produce higher-quality speech.
• Artificial Intelligence (AI): These systems use AI algorithms to generate speech. They are the most advanced type of TTS technology and can produce highly natural and conversational speech.

📚 Benefits of TTS!

GSpeech offers many features, including online, SaaS, on-premise Text-to-Speech (TTS) solutions for a wide variety of sources like websites, mobile apps, e-books, e-learning material, documents, everyday customer experience, transport experience, and a lot more. How a business, organization, and publishers that integrate TTS technology gets benefited.

🎯 Increased Accessibility

TTS technology provides greater accessibility for individuals with visual impairments, dyslexia, or reading difficulties, allowing them to access information and communicate more easily.

🎯 Enhanced SEO

By providing an alternative way for users to consume your content, you can improve your WordPress website's search engine optimization (SEO). This is particularly important for users who rely on screen readers to navigate the web.

🎯 Improved User Experience

TTS technology can enhance the user experience by providing a more natural and intuitive way of interacting with devices, reducing the need for manual typing or reading.

🎯 Enhanced Customer Service

TTS technology can provide 24/7 customer support, answering frequently asked questions and providing information to customers in a more efficient and effective way.

🎯 Increased Productivity

TTS technology can increase productivity by automating tasks such as data entry, transcription, and reading, freeing up time for more important tasks.

🎯 Multilingual Support

TTS technology can support multiple languages, making it a valuable tool for businesses and organizations that operate globally.

🎯 Improved Reading Comprehension

TTS technology can improve reading comprehension by allowing users to listen to text while following along with the written word, making it easier to understand complex information.

🎯 Reduced Eye Strain

TTS technology can reduce eye strain and fatigue by providing an alternative to reading and typing, making it a valuable tool for individuals who spend long hours in front of screens.

🎯 Increased Engagement

TTS technology can increase engagement by providing a more interactive and immersive experience, making it a valuable tool for educational and entertainment applications.

🎯 Competitive Advantage

TTS technology can provide a competitive advantage by offering a unique and innovative way of interacting with devices, setting your product or service apart from the competition.

This has led to a great demand for parametric TTS, where all the information required to generate the data is stored in the parameters of the model, and the contents and characteristics of the speech can be controlled via the inputs to the model. So far, however, parametric TTS has tended to sound less natural than concatenative. Existing parametric models typically generate audio signals by passing their outputs through signal processing algorithms known as vocoders.

WaveNet changes this paradigm by directly modelling the raw waveform of the audio signal, one sample at a time. As well as yielding more natural-sounding speech, using raw waveforms means that WaveNet can model any kind of audio, including music.

WaveNet: A generative model for raw audio

Researchers usually avoid modelling raw audio because it ticks so quickly: typically 16,000 samples per second or more, with important structure at many time-scales. Building a completely autoregressive model, in which the prediction for every one of those samples is influenced by all previous ones (in statistics-speak, each predictive distribution is conditioned on all previous observations), is clearly a challenging task.

However, PixelRNN and PixelCNN models, published earlier, showed that it was possible to generate complex natural images not only one pixel at a time, but one colour-channel at a time, requiring thousands of predictions per image. This inspired us to adapt our two-dimensional PixelNets to a one-dimensional WaveNet.

The above animation shows how a WaveNet is structured. It is a fully convolutional neural network, where the convolutional layers have various dilation factors that allow its receptive field to grow exponentially with depth and cover thousands of timesteps.

At training time, the input sequences are real waveforms recorded from human speakers. After training, we can sample the network to generate synthetic utterances. At each step during sampling a value is drawn from the probability distribution computed by the network. This value is then fed back into the input and a new prediction for the next step is made. Building up samples one step at a time like this is computationally expensive, but we have found it essential for generating complex, realistic-sounding audio.

Improving the State of the Art

We trained WaveNet using some of Google’s TTS datasets so we could evaluate its performance. The following figure shows the quality of WaveNets on a scale from 1 to 5, compared with Google’s current best TTS systems (parametric and concatenative), and with human speech using Mean Opinion Scores (MOS). MOS are a standard measure for subjective sound quality tests, and were obtained in blind tests with human subjects (from over 500 ratings on 100 test sentences). As we can see, WaveNets reduce the gap between the state of the art and human-level performance by over 50% for both US English and Mandarin Chinese.

For both Chinese and English, Google’s current TTS systems are considered among the best worldwide, so improving on both with a single model is a major achievement.

GSpeech has AI voice synthesis algorithm, which is some of the most advanced and realistic in the business. Most voice synthesizers (including Apple’s Siri) use what’s called concatenative synthesis, in which a program stores individual syllables — sounds such as “ba,” “sht,” and “oo” — and pieces them together on the fly to form words and sentences. This method has gotten pretty good over the years, but it still sounds stilted.

WaveNet, by comparison, uses machine learning to generate audio from scratch. It actually analyzes the waveforms from a huge database of human speech and re-creates them at a rate of 24,000 samples per second. The end result includes voices with subtleties like lip smacks and accents. When Google first unveiled WaveNet in 2016, it was far too computationally intensive to work outside of research environments, but it’s since been slimmed down significantly, showing a clear pipeline from research to product.