  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.
