Update README.md

README.md

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-The End-to-end Speech Challenge (ESC) is a benchmark for assessing a single ASR system on a collection of eight different speech recognition datasets. The ESC datasets are sourced from different domains and cover a range of audio and text distributions (speaking styles, background noise, transcription requirements). These distributions are a-priori unknown: systems must use the same training and evaluation algorithms across datasets and may not use any dataset-specific pre- or post-processing. Consequently, ESC encourages generalisable ASR systems that can be applied in a multi-domain setting.
+The End-to-end Speech Challenge (ESC) is a benchmark for assessing a single ASR system on a collection of eight different speech recognition datasets. The ESC datasets are sourced from different domains and cover a range of audio and text distributions (speaking styles, background noise, transcription requirements). The only constraint on systems is that they must use the same training and evaluation algorithms across datasets and may not use any dataset-specific pre- or post-processing, assuming the data distributions are a-priori unknown. The goal of ESC is to promote generalisable ASR systems that can be applied in a multi-domain setting.
 
 ESC consists of: