Emotion-labeled call audio for quality monitoring and escalation models

A call-level sentiment score tells you a conversation went badly. It does not tell a model where, and a model that cannot locate the moment cannot warn a supervisor while the call is still live.

Labeling each speaker turn gives escalation and agent-assist models something to fire on, and gives QA teams a way to audit a review rather than take a single number on faith. Every batch is labeled by several annotators and shipped with per-class inter-annotator agreement, so QA can tell which escalation calls the model is confident about and which ones still need a human read.

Customer calls are usually recorded for service or quality purposes, not for resale into a third party's training corpus. Where the training rights on existing recordings are unclear, the same scenarios can be recorded with recruited, consented speakers and labeled from the start.

Moderated role-play keeps the channel, the vocabulary, and the interruption patterns while leaving room for the genuine frustration a script would flatten out.