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AI voices are everywhere—your phone, your car, even your fridge might be trying to chat with you. Some sound so real that you might wonder if they’re secretly human. But how does this magic happen? How do machines learn to talk like us?
Of course, technology has advanced dramatically, and that’s one reason talking machines have become so impressive. But if we look deeper, we need to ask a more fundamental question: How do machines learn and encode the building blocks of language—sounds?
Buckle up; we’re going on a ride through phonetics, spectrograms, and AI speech training!
There are over 6,000 languages spoken across the world, and each one has its own unique set of sounds. Every native speaker unconsciously masters these sounds, while non-native speakers often struggle with certain ones, that’s why we usually have an accent when we speak a foreign language.
The scientific study of speech sounds is called phonetics. Humans can produce around 600 different consonant sounds and 200 vowel sounds—a massive range of possibilities! But no single language uses them all. For example, English has about 39 sounds (24 consonants, 15 vowels), while Ubykh, an extinct language, had a whopping 86 sounds—84 consonants and just 2 vowels!
The alphabet has given us a way to document human speech, but here’s the problem: it isn’t always reliable when it comes to representing pronunciation. Take English, for instance. The same letter can sound completely different in different words — gym vs. game both start with “g,” but they don’t sound the same. This isn’t just an English problem—it happens in many languages!
Enter the International Phonetic Alphabet (IPA)! This system assigns a unique symbol to every sound human can produce, allowing us to precisely document speech sounds without ambiguity. If you’ve ever seen weird symbols like /θ/ or /ʃ/, congratulations, you’ve met IPA!
Want to play around with weird sounds? Try this interactive chart: IPA Chart
A spectrogram is like an X-ray of sound. It visually represents speech by showing frequency (pitch) on the vertical axis, time on the horizontal axis, and amplitude (sound energy) as varying shades of darkness or color. Within a spectrogram, we can see something called formants.
Formants are concentrations of acoustic energy at specific frequencies in the speech wave, typically spaced around every 1,000 Hz. Each sound has a unique set of formants, and this is how we distinguish them from one another.
According to linguist Peter Ladefoged (2006), vowels typically have three main formants: F1 (related to vowel height), F2 (related to how far forward or back the tongue is), and F3 (which can affect things like rounding). Every IPA symbol represents a unique spectrogram and formant values of the human sounds.
When training a text-to-speech (TTS) model, we provide it with two essential pieces of information:
It’s like giving AI a key and a value for each sound—mapping acoustic features to symbols. By analyzing these patterns, AI learns the characteristics of different sounds and begins to mimic their spectrogram values.
Here’s something fascinating: humans do this instinctively! Our brains are wired to recognize the formant patterns of our native language(s), which is why we instantly notice when someone has an accent. Their formants don’t match what we expect!
Next time you listen to a TTS voice, you’ll know exactly what’s happening behind the scenes. Now that you know how AI learns to talk, click here to try out our Knovvu Text-to-Speech (TTS) voices and see if you can hear their phonetic tricks in action!
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