A Review on Natural Language Processing (NLP) Models for Generating Meeting Transcription

Without a loss of generality, it can be stated that meetings play a vital role in collaboration and decision-making in diverse domains and organizations. However, manually documenting these meetings is time-consuming and prone to errors or omissions. To address this challenge, Natural Language Processing (NLP), a subfield of artificial intelligence, has emerged as a powerful tool for automating the transcription of spoken content. With NLP, meeting conversations can be converted into accurate, readable text in real-time or post-meeting, improving productivity, accessibility, and record-keeping. Almost all automated meeting transcription models use speech recognition, which involves converting spoken language into text. Modern speech recognition systems use machine learning and deep learning models for the purpose. This paper presents a comprehensive review on the machine learning and deep learning models typically involved for NLP and speech transcription. The salient points of the review can be used to leverage speech transcription mechanisms to be used for a specialized case of meeting transcriptions.