With the advancement and increased usage of intelligible smart devices, researchers have an intensified interest in the field of large-vocabulary speaker-independent continuous speech recognition. Although considerable research has been devoted to English speech recognition, less attention has been paid to the Arabic speech recognition. This paper aims to highlight the achievements that have been made during the last several decades of Arabic speech recognition. The paper also discusses speech recognition components such as corpora, phonemes, language models, acoustic models, and performance evaluation. For an empirical evaluation of Arabic speech recognition, the free, off-the-shelf Mac Soundflower tool was employed to evaluate the recognition performance using a continuous speech corpus that contains 2.63 h (by 10 male and 10 female speakers) of modern standard Arabic (MSA) broadcast news. The experimental results indicate recognition accuracy at 54.02%, and the accuracies for the male and female speakers are almost the same. This result promotes the need for further research to expose the practical range of accuracy. The performance’s decline might be an indication of the necessity for further research to boost the overall recognition accuracy.

随着可理解智能设备的进步和使用的增加，研究人员对大词汇量独立于说话人的连续语音识别领域产生了越来越大的兴趣。尽管对英语语音识别进行了大量研究，但对阿拉伯语语音识别的关注较少。本文旨在突出阿拉伯语语音识别在过去几十年中取得的成就。该论文还讨论了语音识别组件，例如语料库、音素、语言模型、声学模型和性能评估。对于阿拉伯语语音识别的实证评估，使用免费的、现成的 Mac Soundflower 工具来评估识别性能，使用包含 2 的连续语音语料库。63 小时（由 10 位男性和 10 位女性演讲者组成）现代标准阿拉伯语 (MSA) 广播新闻。实验结果表明，识别准确率为 54.02%，男性和女性说话者的识别准确率几乎相同。这一结果促进了进一步研究以揭示实际精度范围的需要。性能的下降可能表明需要进一步研究以提高整体识别精度。