We introduce the largest transcribed Arabic speech corpus, QASR, collected
from the broadcast domain. This multi-dialect speech dataset contains 2,000
hours of speech sampled at 16kHz crawled from Aljazeera news channel. The
dataset is released with lightly supervised transcriptions, aligned with the
audio segments. Unlike previous datasets, QASR contains linguistically
motivated segmentation, punctuation, speaker information among others. QASR is
suitable for training and evaluating speech recognition systems, acoustics-
and/or linguistics- based Arabic dialect identification, punctuation
restoration, speaker identification, speaker linking, and potentially other NLP
modules for spoken data. In addition to QASR transcription, we release a
dataset of 130M words to aid in designing and training a better language model.
We show that end-to-end automatic speech recognition trained on QASR reports a
competitive word error rate compared to the previous MGB-2 corpus. We report
baseline results for downstream natural language processing tasks such as named
entity recognition using speech transcript. We also report the first baseline
for Arabic punctuation restoration. We make the corpus available for the
research community.

该论文介绍了目前最大的阿拉伯语语音数据集 QASR，包含 2000 小时的文本和语音数据，可以用于语音识别、语音和 / 或语言学中阿拉伯语方言识别、语音人物识别和潜在的其他 NLP 模块的训练和评估。对于下游 NLP 任务，如命名实体识别和阿拉伯语标点修复，该数据集还提供了基线结果。