Kaikura adds audio based emotion analysis that finally reads what text keeps missing

Shinka just rolled out a chunky upgrade to Kaikura’s emotion labeling and this one actually matters. Until now the platform tried to read customer emotions from call transcripts. That worked on the surface but it missed all the real signals hiding in tone and pacing. The new update listens to the audio itself and pulls emotional cues from pitch, volume and speed. It captures the stuff text cannot show like relief after an issue is fixed or the early tension in a complaint call.

The system breaks emotions into nine buckets for both the customer and the agent. That gives teams a clearer picture of satisfaction levels and even flags agent stress or attitude shifts. It also tracks how emotions change across the beginning, middle and end of each call. That helps managers see whether conversations calm down or spiral and whether the resolution actually landed.

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Kaikura then tags calls with color coded emotional markers so teams can spot the heated stuff, the wins and the follow ups without digging. It fits neatly into the broader trend of AI pushing deeper into real world signals instead of leaning on tidy transcripts.