Text-to-Speech
What is Text-to-Speech?
Text-to-speech is AI that reads text aloud in a natural-sounding voice. You type words in, and the system produces spoken audio out: it can sound like a generic AI voice or, with modern tools, like a specific real person.
At a glance
- Type of model
- Neural speech synthesis model
- Developed by
- Multiple organisations including ElevenLabs, OpenAI, Google, Microsoft, and open-source communities
- Key capability
- Converts written text into natural, expressive spoken audio with controllable voice, tone, and emotion
- How it fits in AI workflow
- Used for voiceover generation, placeholder dialogue, narration, and voice-driven content in AI filmmaking, advertising, e-learning, and interactive media pipelines
- Related terms
- Audio generationVoice cloningSpeech synthesisVoiceoverSound design
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How it compares
Text-to-speech refers to the general capability of synthesising spoken audio from written text, typically using a pre-built or default voice. Voice cloning is a specific advanced application of TTS in which the system replicates the vocal identity of a particular individual from reference recordings, producing output that sounds like that specific person rather than a generic synthesised voice.
Pro tip
For the most natural-sounding TTS output, structure your input text with punctuation that reflects desired speech rhythm: commas and full stops guide pacing more reliably than length of sentence alone: and test multiple voice options on your specific script content, as voice quality varies significantly by text style and subject matter.
Types and variations
- Concatenative TTS stitches together recorded speech segments, producing robotic results and largely superseded by neural approaches.
- Neural TTS uses deep learning models to generate natural-sounding speech end-to-end and is the current standard for quality applications.
- Voice cloning TTS replicates a specific individual's vocal characteristics from reference audio.
- Emotional TTS allows explicit control over the affective quality of synthesised speech.
- Multilingual TTS supports speech generation across many languages from a single model.
- Real-time TTS is optimised for low-latency output suitable for conversational AI and interactive applications.
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Try MorphicCommon use cases
- TTS is used across an enormous range of production and product contexts.
- In AI filmmaking, it generates placeholder voiceover for rough cuts and animatics, and increasingly produces final narration for documentary, explainer, and advertising content.
- In e-learning and corporate training, it populates courses with spoken audio without the cost and logistics of voice talent.
- In broadcasting, it reads financial data, sports results, and news updates automatically.
- In accessibility applications, it enables screen readers and reading assistants for visually impaired users.
- In conversational AI and virtual assistants, real-time TTS provides the spoken output layer of products such as Siri, Alexa, and Claude.
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