标题： N次射击的阿拉伯语语音识别中的Whisper基准测试 显示英文标题

标题： N-Shot Benchmarking of Whisper on Diverse Arabic Speech Recognition

摘要： 最近开发的多语言弱监督模型Whisper在单语和多语设置下的多个语音识别基准测试中表现良好。 然而，即使在阿拉伯语等它已被评估的语言上，也不清楚Whisper在各种条件下的表现如何。 在这项工作中，我们通过在ASR任务中对阿拉伯语语音的多种变体进行全面评估来解决这一差距。 我们的评估涵盖了大部分公开可用的阿拉伯语语音数据，并在n-shot（零次、少量和完整）微调条件下进行。 我们还研究了Whisper在完全新颖条件下的鲁棒性，例如在方言口音的标准阿拉伯语以及我们开发了评估数据的未见过的方言中。 我们的实验表明，尽管Whisper在所有数据集上的零样本设置下优于完全微调的XLS-R模型，但在五个未见过的方言（即阿尔及利亚、约旦、巴勒斯坦、阿联酋和也门）的零样本设置下，其性能显著下降。 显示英文摘要

摘要： Whisper, the recently developed multilingual weakly supervised model, is reported to perform well on multiple speech recognition benchmarks in both monolingual and multilingual settings. However, it is not clear how Whisper would fare under diverse conditions even on languages it was evaluated on such as Arabic. In this work, we address this gap by comprehensively evaluating Whisper on several varieties of Arabic speech for the ASR task. Our evaluation covers most publicly available Arabic speech data and is performed under n-shot (zero-, few-, and full) finetuning. We also investigate the robustness of Whisper under completely novel conditions, such as in dialect-accented standard Arabic and in unseen dialects for which we develop evaluation data. Our experiments show that although Whisper zero-shot outperforms fully finetuned XLS-R models on all datasets, its performance deteriorates significantly in the zero-shot setting for five unseen dialects (i.e., Algeria, Jordan, Palestine, UAE, and Yemen).