New Insights: Expanding Our Voice AI Stack Benchmarks Beyond TTS
Blog post from Coval
A comprehensive analysis of the voice AI stack has been expanded to include both text-to-speech (TTS) and speech-to-text (STT) providers, offering engineering teams performance data to inform architectural decisions for voice applications. The study reveals that the choice of voice AI providers should consider not only audio quality and pricing but also the streaming behavior that affects application performance at scale. The analysis identifies three streaming approaches among major TTS providers: batch-then-stream, true streaming generation, and optimized first-chunk strategies, each with distinct implications for latency and scalability. Additionally, the report highlights the importance of audio format choice and chunk size on streaming performance, recommending PCM/WAV formats over MP3 for latency-sensitive applications. The findings underscore the necessity for technical leaders to benchmark providers against specific requirements and to tailor application architecture according to streaming patterns, buffering strategies, error recovery, and cost modeling, reinforcing that the optimal voice AI architecture is aligned with specific system performance, scale, and user experience goals. The benchmarks provided at benchmarks.coval.ai offer valuable insights beyond summary metrics, emphasizing the critical role of real-world testing in architectural decision-making for voice-first applications.
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