A Benchmarking on Cloud based Speech-To-Text Services for French Speech and Background Noise Effect
Abstract
This study presents a large scale benchmarking on cloud based Speech-To-Text systems: {Google Cloud Speech-To-Text}, {Microsoft Azure Cognitive Services}, {Amazon Transcribe}, {IBM Watson Speech to Text}. For each systems, 40158 clean and noisy speech files about 101 hours are tested. Effect of background noise on STT quality is also evaluated with 5 different Signal-to-noise ratios from 40dB to 0dB. Results showed that {Microsoft Azure} provided lowest transcription error rate on clean speech, with high robustness to noisy environment. {Google Cloud} and {Amazon Transcribe} gave similar performance, but the latter is very limited for time-constraint usage. Though {IBM Watson} could work correctly in quiet conditions, it is highly sensible to noisy speech which could strongly limit its application in real life situations.
Keywords
Cite
@article{arxiv.2105.03409,
title = {A Benchmarking on Cloud based Speech-To-Text Services for French Speech and Background Noise Effect},
author = {Binbin Xu and Chongyang Tao and Zidu Feng and Youssef Raqui and Sylvie Ranwez},
journal= {arXiv preprint arXiv:2105.03409},
year = {2021}
}
Comments
6th National Conference on Practical Applications of Artificial Intelligence, 2021, Bordeaux, France