  Transcriptions of phone calls are of significant value across diverse fields,
such as sales, customer service, healthcare, and law enforcement. Nevertheless,
the analysis of these recorded conversations can be an arduous and
time-intensive process, especially when dealing with extended or multifaceted
dialogues. In this work, we propose a novel method, GPT-distilled Calls
Segmentation and Tagging (GPT-Calls), for efficient and accurate call
segmentation and topic extraction. GPT-Calls is composed of offline and online
phases. The offline phase is applied once to a given list of topics and
involves generating a distribution of synthetic sentences for each topic using
a GPT model and extracting anchor vectors. The online phase is applied to every
call separately and scores the similarity between the transcripted conversation
and the topic anchors found in the offline phase. Then, time domain analysis is
applied to the similarity scores to group utterances into segments and tag them
with topics. The proposed paradigm provides an accurate and efficient method
for call segmentation and topic extraction that does not require labeled data,
thus making it a versatile approach applicable to various domains. Our
algorithm operates in production under Dynamics 365 Sales Conversation
Intelligence, and our research is based on real sales conversations gathered
from various Dynamics 365 Sales tenants.
