This (https://arxiv.org/abs/2010.03449 https://arxiv.org/abs/2010.03449) is by far not the first publication which claims super human speech recognition, or hum

This (https://arxiv.org/abs/2010.03449 https://arxiv.org/abs/2010.03449) is by far not the first publication which claims super human speech recognition, or human parity. For example https://arxiv.org/abs/1610.05256 https://arxiv.org/abs/1610.05256 or https://arxiv.org/abs/1703.02136 https://arxiv.org/abs/1703.02136. And since then, the WER has dropped much more on the same dataset (Switchboard), for example https://arxiv.org/abs/2001.07263 https://arxiv.org/abs/2001.07263. They highlight here the aspect of low latency, so this work might be current SOTA under this condition. Anyway, yes, we have reached human parity, or even surpassed it, for this dataset, under the same conditions. The conditions are: * English telephone speech, mostly no overlapping speech (also 2 speakers max), mostly no background noise (although quality is not great either), speakers are directly next to the microphone (telephone). * You don't know the speakers beforehand, specifically you have never heard their voice or accent beforehand! Both are crucial points. Automatic speech recognition (ASR) models are quite good when there is not too much noise, and when there is not too much speech overlap. In this case, we usually have super human performance. But it's very different when you are somewhere in a room not directly next to the microphone, when there is some background noise, or when multiple people speak at the same time. Then we have not really reached human performance yet. Also, the condition about not having heard the speaker beforehand is special, and maybe artificial. Test that for yourself. You will see that your recognition rate gets quite bad. But once you adapt to it, you will get much better. ASR also can do adaptation, but I think it's not as good at it, i.e. it will get some improvement, but not as much as a human.