“What’s the most human-like text-to-speech?” is a question developers and product managers always ask. Everyone wants a voice that “feels real” – one that sounds natural and engaging to listeners. Voice naturalness directly influences engagement and outcomes. A well-tuned, human-like voice can notably improve customer experience. In high-impact applications like call centers, a more lifelike voice has been shown to increase user trust and even conversion rates.
Defining “Human-Like” Speech and How to Measure It
What does it mean for synthetic speech to sound human-like? At a high level, it means the TTS output has a natural flow with appropriate pauses and inflections, as opposed to a choppy or monotone “robotic” delivery. Several key attributes can be evaluated to gauge naturalness:
Speaking Rate (Speed): Human speakers have an average conversational rate around 150 words per minute. If a TTS voice speaks significantly too fast or too slow, it will sound unnatural. The best models adjust tempo based on context – for example, slowing down for complex sentences or speeding up for simple phrases.
Pauses & Phrasing: Natural speech isn’t a constant stream of words; we pause at commas, periods, and between ideas. A human-like TTS inserts pauses where a human would naturally pause, rather than running words together or pausing awkwardly mid-phrase.
Pitch & Intonation (Prosody): Real human voices have dynamic pitch – we raise or lower our tone for questions, emphasis, or emotion. A flat, monotone delivery is a dead giveaway of a synthetic voice. Good prosody means the voice can emphasize important words, convey excitement or sadness when appropriate.
Beyond these, evaluators also consider pronunciation accuracy, lack of audio artifacts, and even things like backchanneling (e.g. the “uh-huh” and breaths that make a voice sound present in a conversation). The most advanced systems can inject subtle non-verbal cues – laughs, sighs, breaths – to enhance realism.
Measuring these qualities can be done in both objective and subjective ways. Rather than requiring every team to build this infrastructure themselves, Cekura delivers these objective metrics - including speech rate in words per minute (WPM), prosody analysis, and pause detection - out of the box. By researching for months and processing millions of data points, Cekura has also developed robust subjective scoring for aspects like tonality and naturalness, giving engineering teams a comprehensive, ready-to-use QA checklist for voice quality—so they can focus on building, not reinventing the wheel.
Evaluating TTS Quality: Evals vs Real-World A/B Tests
It’s important to build a comprehensive evaluation (eval) suite for TTS models. In offline tests, you can combine objective metrics (like word error rate for pronunciation accuracy, speed etc.) with subjective ratings for naturalness and prosody. Some research-driven evaluations use comparative mean opinion score (CMOS) tests to directly pit one model’s voice against another’s on the same scripts.
However, the real proof of a TTS model’s quality is in production A/B testing. What sounds impressive in a demo may perform differently with real users. Product teams should experiment by serving different voices - varying tone, style, and even accent - to user segments and then measure engagement metrics. For example, one effective metric is early drop-off: do users hang up or stop listening sooner with Voice A versus Voice B? If a more natural or less familiar accent actually keeps users engaged longer or boosts task completion rates, that’s a strong signal of superiority.
Data-driven voice selection often reveals surprises. For instance, “low-energy” voices that seem dull in isolation can outperform upbeat ones in some settings. Similarly, serving voices with a less familiar accent in a particular region can sometimes lead to higher engagement, likely because it stands out or feels more neutral. The key insight: don’t rely on intuition alone. Some of the best-performing TTS voices in IVR menus are those that are less expressive but highly consistent, creating a sense of trust and reliability. Ultimately, combine technical benchmarks with real user feedback and keep iterating until you find what truly “feels real” for your audience.
Leading TTS Models and Platforms in 2025
Here’s a concise overview of the best TTS models for human-like voice quality:
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ElevenLabs – Often considered the gold standard for realistic AI voices, ElevenLabs offers a vast library in many languages, advanced voice cloning, and expressive, context-aware audio. It’s top-rated for clarity and pronunciation, making it ideal for everything from audiobooks to character voices.
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Cartesia – Known for ultra-low latency and clean, natural speech, Cartesia uses state-of-the-art models for real-time applications. It consistently delivers highly natural outputs and gives developers fine-grained control over voice features, making it perfect for voice assistants and interactive use cases.
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Rime Labs – Rime specializes in enterprise and conversational AI, training on real dialogues to capture authentic, relatable speech patterns. Its voices are intentionally “low-energy” for consistency—perfect for virtual agents and IVR systems where reliability and a natural feel matter most.
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Sesame’s CSM – Sesame’s Conversational Speech Model is a powerful, transformer-based TTS model open to everyone. Trained on over a million hours of speech, it excels at expressive, context-aware generation and multi-speaker conversations, advancing open TTS research even if it needs significant compute to run.
Each of these platforms excels in naturalness, expressiveness, and practical usability, setting the bar for modern TTS. In our experience, elevenlabs is still the king of TTS with so much variety and least error rates while Cartesia really nails the latency and personally find the pauses in cartesia much more realistic.
Conclusion
Ultimately, “the best TTS” comes down to what feels most natural for your use case and how you measure success. We’ve seen that naturalness isn’t just a buzzword – it has measurable impact on user engagement and satisfaction. The top TTS models today (from ElevenLabs, Cartesia, Rime Labs, and others) are closing the gap to human speech by mastering speed, pauses, pitch, and all the subtleties of real voices. To choose the right one, you should combine technical evaluation (checking that a voice hits the marks on prosody, clarity, and speaking rate) with A/B testing in production to see how users respond. Don’t be afraid to experiment with different voice styles – the results might surprise you (remember that even a calm, low-energy voice can win over audiences!). By taking a data-driven approach to voice selection and leveraging the latest TTS advancements, you can deliver an AI voice experience that truly feels real to your users. In the end, the “best” TTS model is the one that best convinces your listeners they’re hearing a human and not you.
Naturalness is the north star in this quest, and we now have more tools than ever to reach it.