  In this modern era of technology with e-commerce developing at a rapid pace,
it is very important to understand customer requirements and details from a
business conversation. It is very crucial for customer retention and
satisfaction. Extracting key insights from these conversations is very
important when it comes to developing their product or solving their issue.
Understanding customer feedback, responses, and important details of the
product are essential and it would be done using Named entity recognition
(NER). For extracting the entities we would be converting the conversations to
text using the optimal speech-to-text model. The model would be a two-stage
network in which the conversation is converted to text. Then, suitable entities
are extracted using robust techniques using a NER BERT transformer model. This
will aid in the enrichment of customer experience when there is an issue which
is faced by them. If a customer faces a problem he will call and register his
complaint. The model will then extract the key features from this conversation
which will be necessary to look into the problem. These features would include
details like the order number, and the exact problem. All these would be
extracted directly from the conversation and this would reduce the effort of
going through the conversation again.
