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

arXiv: https://arxiv.org/abs/2105.03409 · HTML: https://mlsanity.com/paper/2105.03409/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Speech: Scaling up end-to-end speech recognition](https://mlsanity.com/paper/1412.5567/) (2014)
- [State-of-the-art Speech Recognition With Sequence-to-Sequence Models](https://mlsanity.com/paper/1712.01769/) (2017)
- [The Microsoft 2017 Conversational Speech Recognition System](https://mlsanity.com/paper/1708.06073/) (2017)
- [A scalable noisy speech dataset and online subjective test framework](https://mlsanity.com/paper/1909.08050/) (2019)
- [Interspeech 2021 Deep Noise Suppression Challenge](https://mlsanity.com/paper/2101.01902/) (2021)
- [English Conversational Telephone Speech Recognition by Humans and Machines](https://mlsanity.com/paper/1703.02136/) (2017)
- [The IBM 2015 English Conversational Telephone Speech Recognition System](https://mlsanity.com/paper/1505.05899/) (2015)

## Built on by (most cited first, 2 of 2)

- [LLM-based speaker diarization correction: A generalizable approach](https://mlsanity.com/paper/2406.04927/) (2024)
- [On Controlled Change: Generative AI's Impact on Professional Authority in Journalism](https://mlsanity.com/paper/2510.19792/) (2025)

## 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.