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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

Technology History How It Works Neural Networks Evolution
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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

KokoroKokoro

Free

Lightweight 82M parameter model delivering studio-quality speech with blazing-fast inference.

Fast 5/5

Best for: State-of-the-art small model — shows how far neural TTS has come

Try Kokoro

BarkBark

Standard

Transformer-based text-to-audio model that generates realistic speech, music, and sound effects.

Slow 4/5

Best for: Transformer-based model demonstrating audio generation beyond speech

Try Bark

CosyVoice 2CosyVoice 2

Standard

Alibaba's scalable streaming TTS with human-parity naturalness and near-zero latency.

Medium 5/5 Voice Cloning

Best for: Streaming TTS with human-parity quality and zero-shot cloning

Try CosyVoice 2

ChatterboxChatterbox

Premium

State-of-the-art zero-shot voice cloning with emotion control from Resemble AI.

Medium 5/5 Voice Cloning

Best for: Zero-shot voice cloning showing the frontier of voice synthesis

Try Chatterbox

Tortoise TTSTortoise TTS

Premium

Multi-voice text-to-speech focused on quality with autoregressive architecture.

Slow 5/5 Voice Cloning

Best for: Autoregressive architecture prioritizing maximum audio quality

Try Tortoise TTS

How Neural TTS Works

The modern speech synthesis pipeline in four steps

1

Understand the Basics

TTS converts written text into spoken audio. Modern systems use neural networks trained on thousands of hours of human speech recordings.

2

Explore Different Models

Each TTS model uses a different architecture (transformer, diffusion, variational) with unique strengths in speed, quality, and features.

3

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.

4

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

Notable systems: Votrax (1970s), DECtalk (1984, used by Stephen Hawking), Apple

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 \

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

Key breakthroughs: WaveNet, Tacotron, FastSpeech, VITS, Bark, Kokoro.

How Modern Neural TTS Works

The architecture behind natural-sounding AI voices

Text Analysis & Normalization

Raw text is cleaned and normalized: numbers become words (\

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

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.

Frequently Asked Questions

Common questions about text to speech technology

TTS stands for Text-to-Speech. It refers to the technology that converts written text into audible spoken words using synthesized or AI-generated voices. The term is used interchangeably with "speech synthesis" in technical literature.

Modern TTS systems work in three stages: text analysis (parsing, normalization, phoneme conversion), prosody prediction (determining rhythm, pitch, stress, and pauses), and audio synthesis (generating the actual sound waveform). Neural models learn all three stages from training data.

Concatenative TTS splices together pre-recorded speech fragments, which can sound choppy at transitions. Neural TTS generates speech from scratch using deep learning, producing smoother, more natural-sounding audio with better prosody and emotion.

SSML (Speech Synthesis Markup Language) is an XML-based markup language that lets you control how TTS systems pronounce text. You can specify pauses, emphasis, pronunciation, pitch changes, and speaking rate using SSML tags within your text input.

TTS is used for accessibility (screen readers for visually impaired users), virtual assistants (Siri, Alexa, Google Assistant), audiobook production, e-learning, GPS navigation, customer service IVR systems, content creation, and language learning applications.

TTS evolved from robotic rule-based systems in the 1960s, to concatenative synthesis in the 1990s, to statistical parametric synthesis in the 2000s, to neural TTS with WaveNet in 2016, to today's transformer and diffusion models that achieve human-level quality.

Natural-sounding TTS requires accurate prosody (rhythm, stress, intonation), appropriate pacing, smooth transitions between phonemes, and consistent voice identity. Neural models learn these patterns from large datasets of natural human speech recordings.

Voice cloning models like Chatterbox and CosyVoice 2 can replicate a specific voice from as little as 5-30 seconds of reference audio. The cloned voice captures timbre, accent, and speaking style, though ethical and legal considerations apply to cloning others' voices.

Modern TTS models collectively support 30+ languages. Some models specialize in specific languages while others are multilingual. English has the most available models and voices, but Chinese, Japanese, Korean, Spanish, and European languages are well-supported.

TTS is a subset of AI voice generation. TTS specifically converts text input to speech output. AI voice generation is a broader term that also includes voice cloning, voice conversion, speech-to-speech, and sound effect generation.

It depends on your needs. Kokoro offers the best balance of speed and quality for general use. Chatterbox leads in voice cloning. Orpheus excels at emotional expression. StyleTTS 2 produces the most natural single-speaker narration. There is no single "best" model for all use cases.

Yes. All models on TTS.ai are open-source and can be self-hosted. CPU-only models like Piper run on any computer. GPU models like Kokoro and Bark need an NVIDIA GPU with 2-8GB VRAM. Our platform also provides hosted access so you don't have to manage infrastructure.
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