模型评估

It is tempting to choose the models based on benchmarks that measure them in laboratory conditions. But real-life conversations are messy, and that makes enterprise-grade voice agents a hard engineering problem. We cannot solve these hard problems by treating voice models as interchangeable commodities. Instead, they must be picked, analysed and measured against their performance for their intended use case .

For voice agents, we believe the metric that matters is TTFS - Time to Final Segment (aka the Time to Complete Transcript (TTCT)).

Turn-detection is also known as end pointing – because it refers to detecting when the user has finished their turn. It is extremely hard to do accurately as Speech to Text models do not have visual or other cues like humans do in conversation. That makes high-quality turn detection incredibly important for conversational AI - poor turn detection results in very long awkward pauses and high latency in the voice agent’s conversation.

Word Error Rate (WER) for STT/ASR is a misleading metric if you’re looking at the wrong or irrelevant classes of errors. Or if you’re comparing a non-streaming ASR model (which is inherently easier to be accurate on) with a streaming ASR model. Unfortunately many benchmarks do not distinguish between ASR streaming vs non-streaming use cases.