Speaker Diarization Accuracy in Real-Time Conversational Voice Systems
Real-time diarization needs better metrics than DER to match what users actually see.
Neural models outperform rule-based detectors in noise while keeping real-time performance intact.
Real-time diarization needs better metrics than DER to match what users actually see.
Provider support for SSML tags varies sharply, forcing developers to test before choosing.
Real-world noise cuts detection accuracy in half compared to clean benchmarks.
Most voice AI misses the 300ms human response window by five times over.
Voice agents need completely new turn-taking models for multiparty conversations.
Real-time barge-in requires coordinating five technical stages, each with its own failure mode.
Streaming TTS failures demand tailored recovery patterns, not generic retries.
Hidden costs beyond listed rates grow exponentially at high volumes.
Web Speech API and cloud TTS require different fixes for buffering and sync.
WebSocket cuts latency in half by eliminating HTTP's per-chunk overhead tax.
Why streaming TTS systems struggle with natural-sounding speech.