Text to speech

We like computers better when they talk back. Not the old GPS voice or the scratchy phone tree. Real voices. Ones that pause in the right places and sound like they’re thinking, even when we know they’re not.

How it works

Text-to-speech (TTS) takes plain text and turns it into audio. Early systems chopped words into bits and glued them together. The result sounded like a robot with a head cold. Today’s models don’t splice—they generate sound wave by wave.

WaveNet

WaveNet is a model from DeepMind. First mention of AI here, so: artificial intelligence (AI) is software that learns patterns from data. After that, she’s just “she.” She reads thousands of voices, then predicts the next blip in an audio wave. String enough blips together and we get a smooth, human-like voice. She’s slower than old systems but sounds far more natural. Think less “Speak & Spell,” more audiobook narrator.

Tacotron

Tacotron takes a different path. She turns text into a picture called a spectrogram. That’s a map of frequencies over time. Then she hands it off to a vocoder (often WaveNet again) that plays it as sound. The two together give us voices that breathe, rise, and fall like ours. Small mistakes still slip in—mispronunciations, odd stresses—but they’re rare compared to the robotic past.

Why it matters

Voices change how we use code. We can build tools that read docs aloud, apps that speak reminders, or games with characters who really talk. The better the voice, the less users notice the machine. That’s the point.

A coder’s note

We chase realism, but a perfect copy of us feels strange. Uncanny, even. So maybe the sweet spot is “human enough.” Like a friend who never loses her voice and never asks for coffee.