Text-to-Speech (TTS) is a technology that enables written text to be converted into spoken words using a synthesized voice that mimics human speech. Simply put, it allows a computer-generated voice to read text aloud in a way that sounds natural and human-like.

Some simple and commonly used examples of text-to-speech include Google Translate, Microsoft Azure TTS, Amazon Polly, Apple's VoiceOver, and screen readers like NVDA or JAWS. These tools are widely used in applications ranging from language learning and accessibility to virtual assistants and navigation systems.

However, traditional computer-generated voices often sound artificial and robotic. With the advancement of deep learning, we can now train sophisticated AI voices that are so natural, even real humans can mistake them for actual human speech.

<aside> 💡

With Deep Learning, we can train sophisticated AI voices that even real humans can mistake them to actual human voices.

</aside>

These advanced systems can capture subtle aspects of human speech, including:

• Emotion (e.g., happiness, sadness, anger).
• Prosody such as stress, pitch, and natural pauses (e.g., at commas, exclamation marks, and quotation marks).
• Intonation, which refers to the rising and falling of the voice, especially at the end of questions or emotional expressions.
• Accent and tone, including regional variations like British, Asian, or Spanish accents.

Most modern text-to-speech systems are hosted and processed in the cloud, allowing for scalable, real-time voice generation.

The Text-to-Speech workflows (oversimplified):

Input: “Hello!! How are you today, Dr. Manner from the 123 Hospital?”

• Text Processing & Linguistic

The text is normalized and analyzed. This step includes expanding abbreviations, handling punctuation, and breaking the text into phonemes

• “Dr” is normalized to “Doctor”; “123” is “One hundred and twenty-three”

• Comma (,) → pause; Exclamation (!!) → emphasis, higher pitch, Question mark (?) → rising intonation at the end

• Exclamation mark and question mark detected

• Machine Learning / Deep Learning Model

Advanced models (e.g., Tacotron, WaveNet, VALL-E) take the processed text and predict how it should sound. They capture natural elements such as intonation, rhythm, pauses, and emotion.