System and method for automated processing of natural language using deep learning model encoding

A computing system for processing natural language, the system comprising:

• at least one electronic processor;
• and
• a non-transitory computer-readable medium storing instructions that, when executed by the at least one electronic processor, cause the at least one electronic processor to perform operations comprising:
• obtaining a first digitally-encoded speech representation corresponding to an agent script for a telecommunication interaction,
• obtaining a second digitally-encoded speech representation corresponding to a voice recording of the telecommunication interaction,
• generating a similarity structure based on the first digitally-encoded speech representation and the second digitally-encoded speech representation, the similarity structure representing a degree of semantic similarity between the first digitally-encoded speech representation and the second digitally-encoded speech representation,
• matching at least one first marker in the first digitally-encoded speech representation to at least one second marker in the second digitally-encoded speech representation based on the similarity structure, wherein matching further comprises identifying discrepancies between an order of markers of first digitally-encoded speech representation and an order of markers of second digitally-encoded speech representation, and
• dividing the telecommunication interaction into a plurality of sections based on the matching.