Voice AI Benchmark: TTS and STT Results via API
Blog post from Coval
Coval is an independent benchmarking platform that evaluates over 55 text-to-speech (TTS) and speech-to-text (STT) models under production-realistic conditions to provide unbiased performance data for voice AI teams. Unlike vendor-supplied benchmarks, Coval uses a consistent dataset, infrastructure, and methodology, ensuring that each model is assessed fairly. The benchmarks focus on key metrics such as Time to First Audio (TTFA) and Word Error Rate (WER) for TTS, and Time to Final Segment (TTFS) and WER for STT, with results updated approximately every 30 minutes and accessible via API. This continuous refresh allows for the detection of changes in model performance due to updates or infrastructure variations, providing real-time insights into latency and accuracy that are crucial for selecting the most suitable models based on specific use case requirements. While Coval does not measure subjective factors like voice naturalness or track pricing due to variability, it offers open-source methodology and comprehensive data that support decision-making processes for model selection, CI/CD integration, and production monitoring within the voice AI industry.
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