Super-Human Performance in Online Low-latency Recognition of Conversational Speech · HackerPapers discussions

Super-Human Performance in Online Low-latency Recognition of Conversational Speech — mentioned by albertzeyer

Thu, 22 Oct 2020 12:15:45 GMT

This (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 or 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. 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…

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Super-Human Performance in Online Low-latency Recognition of Conversational Speech — mentioned by siraben

Thu, 22 Oct 2020 11:23:21 GMT

What I'm curious about is the ability for the AI to cope with transcribing technical vocabulary. Even on the MIT OpenCourseWare lectures the transcription mistakes seem to be highest with unusual technical words (I usually watch them sped up with subtitles on), such as "Van-Emde-Boas tree" being transcribed as "Venom de Boss tree". Or "prologue" instead of "Prolog" in a SICP video. The paper[0] this article refers to doesn't seem to talk about this aspect. [0] https://arxiv.org/pdf/2010.03449.pdf

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