Left gets fewer words wrong. Lower has the final text ready sooner.
Google Chirp 3 is ahead on both datasets together and the Pipecat Dataset, and Smallest Pulse on time to first text and final-text delay. The Ocular Dataset is too close to call.
Last updated Methodology by Dileep Chagam
Lower is better on every measure. Too close to call means the likely ranges overlap. For the wider field, see our pick of the best voice AI APIs for real-time audio.
Google Chirp 3 is more accurate on both datasets. On the 1,000 public Pipecat clips, Google Chirp 3 scores 2.76% and Smallest Pulse 3.59%, so Google Chirp 3 makes fewer errors by 0.83 points. On the eight licensed Ocular recordings, Google Chirp 3 scores 2.00% and Smallest Pulse 3.66%, so Google Chirp 3 makes fewer errors by 1.65 points. Word error rate counts incorrect, missing, and extra words, so lower is better.
Google Chirp 3 is more accurate on both datasets. On the 1,000 public Pipecat clips, Google Chirp 3 scores 2.76% and Smallest Pulse 3.59%, so Google Chirp 3 makes fewer errors by 0.83 points. On the eight licensed Ocular recordings, Google Chirp 3 scores 2.00% and Smallest Pulse 3.66%, so Google Chirp 3 makes fewer errors by 1.65 points. Word error rate counts incorrect, missing, and extra words, so lower is better.
No. Smallest Pulse finishes the transcript 3.42s sooner. Google Chirp 3 has the final transcript 4.48s after the speaker stops and Smallest Pulse 1.06s, at the median. Both are measured on the same 206 streamed turns.
It is a trade-off. Google Chirp 3 makes fewer errors on real conversations, while Smallest Pulse finishes the transcript 3.42s sooner. A voice agent cannot reply until the final transcript arrives, and the Ocular recordings are real conversations.
Yes, both return partial text before the speaker stops. Google Chirp 3 shows first text after 4.19% and Smallest Pulse after 3.46%, at the median.
Cekura runs these simulated calls on your agent and scores every one.