TTS Model Comparison
Compare performance metrics between different Text-to-Speech models for voice agent applications.
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Latency Benchmarks
Time to First Audio
Delivering natural and responsive voice agents requires both speed and consistency. At Coval, we understand that latency is critical for realistic conversations, which is why we go beyond average measurements to track comprehensive percentile metrics with continuous 15-minute evaluation cycles. This rigorous approach ensures your voice AI maintains the reliable performance necessary for engaging user experiences.
Latency Variation
Distribution of TTFA values across all runs
Narrow distributions indicate reliable, predictable response times, while wide distributions show erratic performance that may frustrate users despite good average speeds. A model with moderate median latency and tight distribution often provides superior user experience compared to a faster median model with high variability.
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Latency vs Accuracy
Average TTFA and WER across all measurements • Bubble size represents price
Every voice AI system faces a fundamental trade-off between speed and accuracy. Faster models might sacrifice precision to deliver quick responses, while more accurate models may take additional processing time to ensure correct results. Choose the model that offers the best balance for your specific use case.
Accuracy by Model
Word Error Rate (%) • Click bar to compare models
Ensuring accurate speech output is fundamental to user trust and comprehension in voice AI systems. We recognize that even minor pronunciation errors can undermine the entire conversation experience and our evaluation captures how faithfully text-to-speech systems pronounces complex terminology, proper nouns, and domain-specific vocabulary that matter most to your users.
Model Performance Heatmap
Comprehensive model performance comparison • Click column headers to sort by metric
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