Google TTS vs Cartesia vs Shunya: Best TTS Model?
Text-to-speech (TTS) technology has improved dramatically over the past few years.
What once sounded robotic and unnatural can now sound remarkably human.
Today, AI voices can answer customer support calls, narrate videos, power voice assistants, and even conduct entire conversations through voice agents.
But not all AI voices sound the same.
Some are expressive and natural. Others still struggle with pronunciation, pacing, emotional tone, or multilingual speech.
This raises an important question:
Which text-to-speech platform actually sounds the most natural?
Let’s comparing three leading systems:
- Google TTS
- Cartesia
- Shunya TTS
The results reveal some interesting insights about what makes AI-generated speech sound human.
Why Naturalness Matters in Text-to-Speech
For many applications, transcription accuracy is only half the equation.
The other half is how the voice sounds.
A customer interacting with a voice agent forms an opinion within seconds.
If the voice sounds robotic, users may:
- Hang up earlier
- Trust the system less
- Become frustrated
- Switch to human support
This is particularly important in:
- Customer support
- Healthcare
- Education
- Banking
- Conversational AI
- Voice assistants
As voice AI becomes more common, naturalness is becoming a competitive advantage.
The TTS Models We Compare Today
Google TTS
Google’s text-to-speech platform is one of the most widely used speech generation systems in the world.
Its strengths include:
- Broad language coverage
- Strong infrastructure
- Enterprise adoption
- Reliable performance
Cartesia
Cartesia has gained attention for low-latency speech generation and conversational AI applications.
The platform focuses heavily on real-time voice experiences and AI agent use cases.
Shunya TTS
Shunya’s text-to-speech platform was designed with multilingual speech generation in mind.
The platform supports:
- 55+ languages
- Multiple speaking styles
- Regional language coverage
- Real-time voice generation
- Code-switched speech
How to Evaluate TTS Models
Rather than measuring latency or synthetic benchmarks, when evaluating TTS models, we should focus on something more important:
Human perception.
Listen to audio samples generated by each system and rank them based on naturalness.
Things to look out for:
- Multiple languages
- Different speakers
- Diverse content samples
The goal should be simple:
Which voice sound the most natural to human listeners?
Top TTS Models
Average Naturalness Ranking
| Platform | Average Ranking |
|---|---|
| Shunya TTS | 1.90 |
| Google TTS | 2.04 |
| Cartesia | 2.06 |
Based on the blind rankings by 31 evaluators across 23 languages, Shunya Labs TTS achieved the highest overall score among the three systems tested.
Shunya Labs is the first ever synthesis for 32 low resource Indian languages.
What Makes a Voice Sound Natural?
Most users cannot explain why one voice feels more human than another.
However, several factors play an important role.
Prosody
Prosody refers to rhythm, stress, and intonation.
Human speech naturally varies in pitch and pacing.
Monotone delivery often makes synthetic speech sound robotic.
Pronunciation
Many systems perform well on English content but struggle with regional names, multilingual phrases, and local pronunciations.
This becomes especially important in countries such as India, where conversations frequently include multiple languages.
Pausing and Timing
Human speakers pause naturally.
Poorly timed pauses can immediately reveal that a voice is AI-generated.
Emotional Expression
Modern speech systems increasingly support different speaking styles.
For example:
- Conversational
- Professional
- Empathetic
- Energetic
The ability to adjust tone improves realism.
The Challenge of Multilingual Speech
Generating natural English speech is difficult.
Generating natural speech across dozens of languages is even harder.
Every language has unique characteristics.
For example:
- Hindi uses different stress patterns than English.
- Tamil has distinct phonetic structures.
- Bengali relies on different vowel sounds.
- Code-switched speech introduces additional complexity.
This is why multilingual voice generation remains one of the most challenging areas of speech AI.
Why Voice Quality Matters for AI Agents
The rise of voice agents is changing how businesses think about speech generation.
A voice agent may speak thousands of times per day.
Small quality improvements quickly become noticeable at scale.
Organizations deploying voice agents increasingly evaluate:
- Naturalness
- Latency
- Language coverage
- Pronunciation accuracy
- Customization options
Rather than focusing only on speech generation speed.
Beyond Benchmarks: Choosing the Right Platform
The best text-to-speech platform depends on your requirements.
Choose Google TTS if:
- You need mature cloud infrastructure.
- Global deployment is a priority.
- Broad language coverage is sufficient.
Choose Cartesia if:
- Real-time conversational applications are your primary focus.
- Ultra-low latency matters most.
Choose Shunya Labs if:
- Naturalness is a priority.
- Multilingual support is important.
- Indian and regional language quality matters.
- Voice agents are part of your roadmap.
The Future of AI Voices
The next generation of speech systems will not be judged solely on whether they sound human.
They will be judged on whether they communicate like humans.
That includes:
- Natural pacing
- Emotional expression
- Regional pronunciation
- Multilingual fluency
- Context awareness
As voice AI becomes more common across customer support, healthcare, education, and enterprise applications, naturalness will become one of the most important measures of quality.
The gap between synthetic and human speech continues to shrink, and the companies that can bridge that gap most effectively will shape the future of conversational AI.
Frequently Asked Questions
Which text-to-speech platform sounds the most natural?
In the blind evaluation analyzed in this article, Shunya achieved the best average naturalness ranking among the tested platforms.
What makes an AI voice sound natural?
Naturalness depends on pronunciation, pacing, intonation, emotional expression, and the ability to handle multilingual speech correctly.
Why is multilingual text-to-speech difficult?
Each language has unique phonetics, rhythm, pronunciation rules, and cultural nuances that speech systems must learn.
Is low latency more important than naturalness?
It depends on the application. Customer-facing voice agents typically require both. A fast response is valuable, but users may disengage if the voice sounds unnatural.
What industries use AI-generated voices?
Customer support, healthcare, education, banking, media, accessibility tools, and conversational AI platforms are among the largest adopters.
Bio: Navvya works at the intersection of product strategy and applied AI research at Shunya Labs. With a background in human behaviour and communication, she writes about the people, markets, and technology behind voice AI, with a particular focus on how speech interfaces are reshaping access across emerging markets.