Integration of Text Classification Model with Speech to Text System

摘要

In the services industry chat helplines were seen as more effective than a voice based service because more number of users could be serviced at the same time and with help of standard text message templates. By training text classifier models and integrating them text to speech conversion systems we can further reduce human effort and thereby deliver efficient solutions with minimal participation and increase user convenience multifold. Our proposed system is the integration of an efficiently trained text classifier model with an open source speech to text conversion plat-form. Our trained model can receive the input in text format from the con-version tool and can accurately classify its category (i.e label it). Based on its classification, consequent action is initiated. Our trained model will eliminate the need for agents manually processing the conversation and initiating required action. The system can save lot of energy, time and other resources.