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A Benchmarking on Cloud based Speech-To-Text Services for French Speech and Background Noise Effect

2021-05-07 17:41:34
Binbin Xu, Chongyang Tao, Zidu Feng, Youssef Raqui, Sylvie Ranwez
arXiv_CL
   
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 $9.09\%$ 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.

Abstract (translated)

URL

https://arxiv.org/abs/2105.03409

PDF

https://arxiv.org/pdf/2105.03409.pdf

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