An Intelligent Framework for Person Identification Using Voice Recognition and Audio Data Classification

Abstract The paper proposes a framework to record meeting to avoid hassle of writing points of meeting. Key components of framework are “Model Trainer” and “Meeting Recorder”. In model trainer, we first clean the noise in audio, then oversample the data size and extract features from audio, in the end we train the classification model. Meeting recorder is a post-processor used for sound recognition using the trained model and converting the audio into text. Experimental results show the high accuracy and effectiveness of the proposed implementation.