Detection and Diarization of Speech Processing Using Transformer Model

In this paper, we focus on the implementation of machine learning algorithms for speaker diarization and transcription in audio processing systems. Background: Speech detection and diarization play a crucial role in modern speech processing applications, such as virtual assistants, transcription services, and meeting analytics. Diarization answers the question of ”who spoke when,” enabling the identification and segmentation of individual speakers in a multi-speaker audio environment. Traditional methods struggled with overlapping speech, noise interference, and similar voice characteristics, but advancements in machine learning and deep learning have significantly improved accuracy. This paper focuses on leveraging cutting-edge techniques, including xvector embeddings, spectral clustering, and cloud integration, to achieve effective diarization. Objective(s): The primary objective of this paper is to build an efficient speech detection and diarization system that can accurately identify and separate speakers in an audio file. The system aims to enhance usability by integrating cloud-based storage and providing easy access to results for analysis.