If you watch the demo video you can see how they would get this: the model is not aggressive enough. While it doesn't cut you off, which is nice, it also always waits an uncanny amount of time to chime in.

That should lead to a low recall: too many false negatives. I wonder how they are calculating it.

The response timing in the chart in the blog post shows that even with perfect precision/recall Sparrow-1 also has the fastest true positive response times.

The turn taking models were evaluated in a controlled environment with no additional cascaded steps: LLM, TTS, Phx. This matters to get apples to apples comparison: without the rest of the pipeline variability influencing the measurements.

The video conversation examples are sparrow-1 within the full pipeline. These responses aren’t as fast as sparrow itself because the LLM, TTS, facial rendering, and network transport also take time. Without Sparrow-1 they would be slower. Sparrow-1 enables the responses being as fast as they are, and with a faster CVI pipeline configuration the responses can be as fast as 430ms in my testing.