Topics, Questions, Action Items and Follow-Ups
Call center agents often need to write an overview after each call, describing the main issues and how they were resolved. Cobalt’s Call Summarization project leverages multiple machine learning techniques:
- To identify the most relevant lines from the call transcript, tagging them as topics, customer questions, agent actions, and follow-ups
- To draft a human-friendly paragraph summarizing the entire call
The agent can greatly reduce their post-call processing time by accepting or lightly editing the suggested summary rather than having to write it from scratch.
Acoustic Events TM
Gaining Intelligence Through Sound and Acoustic Events
In addition to speech, Cobalt can train models to recognize other sounds. One lab project built a model to recognize sounds that could be useful for identifying security incidents, such as breaking glass, screaming, alarms, etc.
Imagine a Public Security System of detecting dreams, thuds, crash sounds, etc.