Audio vs transcript analytics: tone, empathy and text
What a call's text establishes reliably, what needs the audio, and what neither shows. How to turn subjective criteria into observable behaviour.
This section covers the technical and reliability side of call analysis: a pilot on existing recordings, the fields that should accompany a recording, how poor audio distorts scores, separating agent and customer voices, testing in Azerbaijani, Russian and English, what text and audio can and cannot establish, protecting personal data, and measuring accuracy properly.
The articles are for QA, IT and operations managers. What the product actually does is set out on the Vexvon Audio Analyzer page; to run a pilot on your own recordings, get in touch.
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What a call's text establishes reliably, what needs the audio, and what neither shows. How to turn subjective criteria into observable behaviour.
Noise, dropouts and incomplete recordings can turn a mistake the agent never made into a low score. When to stop or limit the evaluation of a call.
A diarization error attributes the customer's words to the agent and creates a false violation. Stereo vs mono, sensitive criteria and a test method.
Before sending call recordings to an AI analysis system: who sees what, how long it is kept, how masking is checked and how files are deleted.
A small pilot before rolling call analysis out to the whole team: goal, representative sample, human reference, success criteria and the final decision.
Agent scoring in Azerbaijani, Russian and English has to be tested per language and language mix. Test set, translation and fair comparison.
An audio file does not carry the call's context. Which fields should accompany a recording for quality analysis, and what breaks when each is missing.
AI call analysis accuracy is not one number: transcript quality, per-criterion agreement, false flags, missed violations and calls that cannot be analysed.
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