SPEECH-TO-TEXT TRANSCRIPTION FOR CORPORATION GRIEVANCE CALLS

Abstract

To evaluate and enhance an existing open-source speech-to-text transcription tool for accurately converting feedback calls about citizen grievances into English text. The focus lies on achieving measurable improvements in transcription accuracy across calls made in Tamil or English. Rather than creating a new system, the project concentrates on refining an established open-source solution. The endeavor spans domains including Natural Language Processing (NLP), speech recognition algorithms, and machine learning. Through this effort, the objective is to enhance the tool's efficacy in processing multilingual data and better serve the needs of diverse linguistic communities in citizen grievances.