Why it matters
A transcript tells you what was said. Caller sentiment and tone can help a reviewer locate a tense exchange or examine whether a problem remained unresolved.
Treat these labels as clues. The reviewer checks the words and audio to understand the conversation before deciding what to do.
How it works
- 01
Read the words
Text sentiment provides context alongside transcript turns.
- 02
Listen to the moment
Separate audio analysis adds acoustic context. The reviewer can play the corresponding part of the call.
- 03
Keep scoring about behavior
Score observable actions, such as explaining a policy or acknowledging a request. Keep sentiment and tone out of individual QA grades.
What’s included
Text and audio context
See separate views of the conversation rather than reducing it to one emotion label.
Transcript and playback
Check the surrounding exchange and hear the recording when a label needs interpretation.
Your data
Text and audio analysis run on customer-controlled hardware in the local workflow. Your access and retention rules apply to the results as well as the recordings.
Before you roll out
Check speaker attribution
Confirm that caller and agent turns are assigned correctly before interpreting a caller signal.
Check real recording conditions
Background noise, accents and channel layout can affect the results. Test the calls your reviewers actually receive.
Availability
- Caller context is included in the free local platform.
- Model labels are uncertain interpretations; they do not establish a person’s mental state or intent.
Questions for your team
- 01
Does the context help reviewers find relevant moments?
- 02
Does the label agree with the words and audio in that exchange?
- 03
Are all agent scorecard criteria based on observable behavior?