Recent advances in automatic speech recognition (ASR) have achieved accuracy
levels comparable to human transcribers, which led researchers to debate if the
machine has reached human performance. Previous work focused on the English
language and modular hidden Markov model-deep neural network (HMM-DNN) systems.
In this paper, we perform a comprehensive benchmarking for end-to-end
transformer ASR, modular HMM-DNN ASR, and human speech recognition (HSR) on the
Arabic language and its dialects. For the HSR, we evaluate linguist performance
and lay-native speaker performance on a new dataset collected as a part of this
study. For ASR the end-to-end work led to 12.5%, 27.5%, 33.8% WER; a new
performance milestone for the MGB2, MGB3, and MGB5 challenges respectively. Our
results suggest that human performance in the Arabic language is still
considerably better than the machine with an absolute WER gap of 3.5% on
average.

这篇文章探讨了自动语音识别方面的技术进展，并比较了针对阿拉伯语和其方言的端到端 Transformer ASR，模块化 HMM-DNN ASR 和人类讲话识别技术之间的性能差距，结果表明，人类在阿拉伯语方面表现仍然明显优于机器。