An optimized approach to speech transcription using blind source separation and speech-to-text machine learning models

Abstract

The use of speech-to-text transcription has a multitude of applications in various industries, including accessibility support, language processing, and automatic subtitling. In recent years, there has been greater interest in incorporating automatic speech source separation features to improve the accuracy and efficiency of transcription mechanisms. This paper aims to design a transcription mechanism that utilizes DUET algorithm to separate speech sources in a stereo setup. The separated sources are then transcribed into text using a machine learning model. The study evaluates the effectiveness of this approach using a dataset of speech recordings. The results of the study indicate high accuracy in speech separation and transcription, highlighting the potential of this approach for practical applications. However, the study also revealed potential issues with the mechanism, indicating the need for further exploration and refinement. These findings indicate the potential of the proposed approach for practical applications, and propose insight for further development and researches in this area.