Real-time transcription for contact centers: what latency and accuracy thresholds matter

Real-time speech-to-text (STT) for contact centers needs to balance latency and accuracy, with sub-300ms latency aligning with human conversational pauses, yet focusing solely on speed can lead to errors that degrade the product. The latency budget encompasses audio capture, STT inference, natural language understanding (NLU), and text-to-speech (TTS), with each step consuming a portion. Partial transcript stability is crucial as intermediate outputs influence IVR routing and agent assist, and frequent changes can cause misrouting and irrelevant prompts. While many teams prioritize speed, issues arise when transcripts are fast but inaccurate, impacting agent assist and customer satisfaction scores (CSAT). Real-time transcription differs from batch processing, as it streams partial outputs, which downstream systems use immediately. For effective real-time applications, the focus should be on achieving stable, actionable transcripts within the natural pause window. Models like Solaria-1, optimized for multilingual and noisy environments, offer approximately 270ms responsiveness, supporting over 100 languages, which is beneficial for global contact centers. Evaluating STT providers requires testing on authentic contact center audio, ensuring sub-300ms latency targets while considering additional costs and conducting a real-world pilot to measure performance under production conditions.