What is Text to Speech (TTS)?
Text to speech is the technology that converts written text into spoken audio using artificial intelligence. From early robotic synthesizers to today's neural networks that sound indistinguishable from humans, TTS has transformed how we interact with technology, consume content, and make information accessible.
Key Concepts in Text to Speech
Understanding the building blocks of modern speech synthesis
What TTS Stands For
TTS stands for Text-to-Speech — the technology that converts written text into spoken audio using computer-generated voices.
How Neural TTS Works
Modern TTS uses deep neural networks to analyze text, predict speech patterns, and generate audio waveforms that sound remarkably human.
History of Speech Synthesis
From 1960s rule-based systems to 1990s concatenative synthesis to today's neural models — how TTS evolved over six decades.
Modern AI Models
Today's models like Kokoro, Bark, and CosyVoice 2 use transformers, diffusion, and variational inference to achieve human-level speech quality.
Common Applications
TTS powers screen readers, GPS navigation, virtual assistants, audiobooks, customer service bots, e-learning platforms, and content creation.
Open Source vs Commercial
Open-source models (MIT, Apache 2.0) provide free, self-hostable TTS while commercial services offer managed APIs with SLAs and support.
TTS Models Available on TTS.ai
From fast and lightweight to studio-quality neural voices
Kokoro
Free
Lightweight 82M parameter model delivering studio-quality speech with blazing-fast inference.
Najbolje za: State-of-the-art small model — shows how far neural TTS has come
Pokušaj. Kokoro
Bark
Standard
Transformer-based text-to-audio model that generates realistic speech, music, and sound effects.
Najbolje za: Transformer-based model demonstrating audio generation beyond speech
Pokušaj. Bark
CosyVoice 2
Standard
Alibaba's scalable streaming TTS with human-parity naturalness and near-zero latency.
Najbolje za: Streaming TTS with human-parity quality and zero-shot cloning
Pokušaj. CosyVoice 2
Chatterbox
Premium
State-of-the-art zero-shot voice cloning with emotion control from Resemble AI.
Najbolje za: Zero-shot voice cloning showing the frontier of voice synthesis
Pokušaj. Chatterbox
Tortoise TTS
Premium
Multi-voice text-to-speech focused on quality with autoregressive architecture.
Najbolje za: Autoregressive architecture prioritizing maximum audio quality
Pokušaj. Tortoise TTSHow Neural TTS Works
The modern speech synthesis pipeline in four steps
Understand the Basics
TTS converts written text into spoken audio. Modern systems use neural networks trained on thousands of hours of human speech recordings.
Explore Different Models
Each TTS model uses a different architecture (transformer, diffusion, variational) with unique strengths in speed, quality, and features.
Try It Yourself
The best way to understand TTS is to use it. Try our free models above — paste any text and hear it spoken in seconds.
Integrate Into Your Projects
Once you find a model you like, use our API to integrate TTS into your applications, products, or content creation workflow.
A Brief History of Text to Speech
From mechanical talking machines to neural networks
Early Days (1950s-1980s)
The first computer-generated speech dates back to 1961, when IBM's John Larry Kelly Jr. demonstrated a speech synthesizer at Bell Labs that sang "Daisy Bell" — inspiring the famous HAL 9000 scene in 2001: A Space Odyssey. Early systems used formant synthesis, generating sound by modeling the resonant frequencies of the human vocal tract. The results were intelligible but distinctly robotic.
Notable systems: Votrax (1970s), DECtalk (1984, used by Stephen Hawking), Apple's MacinTalk (1984).
Concatenative Synthesis (1990s-2000s)
Concatenative TTS records a real human voice speaking thousands of phoneme combinations, then stitches together the right segments at runtime. This produced more natural-sounding speech but required massive databases (often 10-20 hours of recordings per voice). Quality depended heavily on finding smooth joins between segments.
Used by: AT&T Natural Voices, Nuance Vocalizer, early Google Translate TTS.
Statistical/Parametric (2000s-2010s)
Instead of stitching recordings, parametric models learned statistical representations of speech. Hidden Markov Models (HMMs) and later deep neural networks generated speech parameters (pitch, duration, spectral features) that were fed through a vocoder. This allowed unlimited vocabulary and easier voice creation, but the vocoder step often produced a "buzzy" quality.
Key models: HTS, Merlin, early DNN-based systems.
Neural TTS (2016-Present)
The modern era began with WaveNet (DeepMind, 2016), which generated audio sample by sample using deep neural networks. This was followed by Tacotron (Google, 2017), which learned to map text directly to spectrograms. Today's models like VITS, Tortoise, and Kokoro produce speech virtually indistinguishable from human recordings, with natural prosody, emotion, and rhythm.
Key breakthroughs: WaveNet, Tacotron, FastSpeech, VITS, Bark, Kokoro.
How Modern Neural TTS Works
Arhitektura iza prirodnih glasova umjetne inteligencije
Text Analysis & Normalization
Raw text is cleaned and normalized: numbers become words ("42" becomes "forty-two"), abbreviations are expanded ("Dr." becomes "Doctor"), and punctuation is interpreted for pauses and intonation. The text is then converted to phonemes — the individual sound units of language. This stage also handles homographs (words spelled the same but pronounced differently based on context, like "lead").
Acoustic Model (Text to Spectrogram)
The acoustic model (often a Transformer or autoregressive network) takes the phoneme sequence and predicts a mel spectrogram — a visual representation of how the audio's frequency content changes over time. This is where prosody (rhythm, stress, intonation) is determined. Models like Tacotron 2 use attention mechanisms to align text with audio timing naturally.
Vocoder (Spectrogram to Audio)
The vocoder converts the mel spectrogram into actual audio waveforms. Early vocoders like Griffin-Lim produced robotic artifacts. Modern neural vocoders (HiFi-GAN, BigVGAN, Vocos) generate high-fidelity 24kHz or 44.1kHz audio that captures the fine details of natural speech, including breath sounds and subtle lip movements.
End-to-End Models
The latest models like VITS, Kokoro, and Bark skip the two-stage pipeline entirely. They go directly from text to audio in a single neural network, producing more natural results with fewer artifacts. Some models (like Bark) can even generate non-speech sounds, laughter, and music alongside speech.
TTS Approaches Compared
How the four generations of TTS technology compare
| Approach | Era | Naturalness | Flexibility | Speed | Data Needed |
|---|---|---|---|---|---|
| Formant Synthesis Rule-based frequency modeling |
1960s-1990s | None | |||
| Concatenative Stitched audio segments |
1990s-2010s | 10-20+ hours | |||
| Parametric (HMM/DNN) Statistical speech models |
2000s-2016 | 1-5 hours | |||
| Neural End-to-End Deep learning (VITS, Kokoro, Bark) |
2016-Present | Minutes to hours |
Common Applications of TTS
Where text to speech is used today
Accessibility
Screen readers, assistive devices, and tools for people with visual impairments or reading disabilities rely on TTS to make digital content accessible to everyone.
Content Creation
YouTubers, podcasters, and social media creators use TTS for voiceovers, narration, and automated content production at scale.
Virtual Assistants
Siri, Alexa, Google Assistant, and customer service chatbots all use TTS to speak responses naturally to users.
Često postavljana pitanja
Common questions about text to speech technology
Experience Modern TTS Yourself
Try 24+ state-of-the-art AI voice models for free. See how far text to speech has come.