WeScribe: An Intelligent Meeting Transcriber and Analyzer Application

In all existing organizations regardless of their type or size, meetings are conducted on the regular basis to invite discussions for organizational decision making. While many organizations, even large ones, still hire employees to perform these tasks, there is no doubt that the results are exposed to human error. Documenting meetings’ minutes is essential for its success and keeping track of the work progress and decisions flow, approvals, while keeping it complete, consistent, and coherent. This project idea was proposed by Aramco to develop a suitable solution for a hectic problem. The process of documenting and taking minutes can be tedious, so we aim to automate audio meeting transcription with the use of technologies that convert speech to text while recognizing the speaker and then process and analyze the most valuable information tagged based on persons in the meeting. This goal can be accomplished through the development of an app that uses speech recognition for conversion, voice recognition for identification of speakers, and natural language processing (NLP) for analysis and then combines them all in a transcription form with considerable accuracy. Further, the proposed system identifies potential events, deadlines, and follow-ups and adds them to the speaker’s calendar upon approval. In the future, we aspire to expand it with some features such as increasing the number of meeting members, creating special sections for each department in the company which adopt WeScribe, and feed our NLP model with more data to develop its performance and increase its accuracy.