Benchmarking open source and paid services for speech to text: an analysis of quality and input variety.

Abstract

Speech to text (STT) technology has seen increased usage in recent years for automating transcription of spoken language. To choose the most suitable tool for a given task, it is essential to evaluate the performance and quality of both open source and paid STT services. In this paper, we conduct a benchmarking study of open source and paid STT services, with a specific focus on assessing their performance concerning the variety of input text. We utilizes ix datasets obtained from diverse sources, including interviews, lectures, and speeches, as input for the STT tools. The evaluation of the instruments employs the Word Error Rate (WER), a standard metric for STT evaluation. Our analysis of the results demonstrates significant variations in the performance of the STT tools based on the input text. Certain tools exhibit superior performance on specific types of audio samples compared to others. Our study provides insights into STT tool performance when handling substantial data volumes, as well as the challenges and opportunities posed by the multimedia nature of the data. Although paid services generally demonstrate better accuracy and speed compared to open source alternatives, their performance remains dependent on the input text. The study highlights the need for considering specific requirements and characteristics of the audio samples when selecting an appropriate STT tool.