A Benchmarking on Cloud based Speech-To-Text Services for French Speech and Background Noise Effect
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 7 May 2021.
Builds on
7 of 13 references.
References · 7
- 2014Deep Speech: Scaling up end-to-end speech recognitioncited by 354
- 2017State-of-the-art Speech Recognition With Sequence-to-Sequence Modelscited by 196
- 2017The Microsoft 2017 Conversational Speech Recognition Systemcited by 51
- 2019A scalable noisy speech dataset and online subjective test frameworkcited by 47
- 2021Interspeech 2021 Deep Noise Suppression Challengecited by 43
- 2017English Conversational Telephone Speech Recognition by Humans and Machinescited by 40
- 2015The IBM 2015 English Conversational Telephone Speech Recognition Systemcited by 6
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.