Handling and extracting key entities from customer conversations using Speech recognition and Named Entity recognition

TL;DR

This paper presents a two-stage system combining speech-to-text conversion and Named Entity Recognition using BERT to extract key customer details from conversations, enhancing customer service efficiency.

Contribution

It introduces a novel pipeline integrating speech recognition and NER for extracting critical customer information from spoken interactions.

Findings

Effective extraction of key entities like order number and problem description.

Reduced manual effort in analyzing customer conversations.

Improved accuracy with a robust two-stage model.

Abstract

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.