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

• URL: http://arxiv.org/abs/2211.17107v1
• Date: Mon, 28 Nov 2022 06:41:29 GMT
• Title: Handling and extracting key entities from customer conversations using Speech recognition and Named Entity recognition
• Authors: Sharvi Endait, Ruturaj Ghatage, Prof. DD Kadam
• Abstract summary: It is very important to understand customer requirements and details from a business conversation. Extracting key insights from these conversations is very important when it comes to developing their product or solving their issue. For extracting the entities we would be converting the conversations to text using the optimal speech-to-text model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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