QASR : QCRI Aljazeera Speech Resource A Large Scale Annotated A rabic Speech Corpus (2021)

Abstract: 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.

## introduction research on automatic speech recognition (asr) has attracted a lot of attention in recent years @xcite . such success has brought remarkable improvements in reaching human-level performance @xcite @xcite . this has been achieved by the development of large spoken corpora: supervised @xcite ; semi-supervised @xcite ; and more recently unsupervised @xcite transcription. this work enables to either reduce word error rate (wer) considerably or extract metadata from speech: dialect-identification @xcite ; speaker-identification @xcite ; and codeswitching @xcite . natural language processing (nlp), on the other hand values large amount of textual information for designing experiments. nlp research for arabic has achieved a milestone in the last few years in morphological disambiguation, named entity recognition (ner) and diacritization @xcite @xcite . the nlp stack for modern standard arabic (msa) has reached very high performance in many tasks. with the rise of dialectal arabic (da) content online, more resources and models have been built to study da textual dialect identification @xcite . our objective is to release the first arabic speech and nlp corpus to study spoken msa and da. this is to enable empirical evaluation of learning more than the word sequence from the speech. in our view, existing speech and nlp corpora are missing the link between the two different modalities. speech poses unique challenges such as disfluency (pravin and palanivelan, 2021), overlap speech @xcite , hesitation @xcite , and code-switching @xcite . these challenges are often overlooked when it comes to nlp tasks, since they are not present in typical text data. in this paper, we create and release 2 the largest corpus for transcribed arabic speech. it comprises of 2, 000 hours of speech data with lightly supervised transcriptions. our contributions are: (i) aligning the transcription with the corresponding audio segments including punctuation for building asr systems; (ii) providing semi-supervised speaker identification and speaker linking per audio segments; (iii) releasing baseline results for acoustic and linguistic arabic dialect identification and punctuation restoration; (iv) adding a new layer of annotation in the publicly available mgb-2 testset, for evaluating ner for speech transcription; (v) sharing code-switching data between arabic and foreign languages for speech and text; and finally, (vi) releasing more than 130m words for language model (lm). we believe that providing the research community with access to multi-dialectal speech data along with the corresponding nlp features will foster open research in several areas, such as the analysis of speech and nlp processing jointly. here, we build models and share the baseline results for all of the aforementioned tasks. ## conclusion in this paper, we introduce a 2, 000 hours transcribed arabic speech corpus, qasr. we report results for automatic speech recognition, arabic dialect identification, speaker verification, and punctuation restoration to showcase the importance and usability of the dataset. qasr is also the first arabic speech-nlp corpus to study spoken modern standard arabic and dialectal arabic. we report for the first time named entity recognition in arabic news transcription. the 11, 092 unique speakers present in qasr can be used to study turn-taking and speaker dynamics in the broadcast domain. the corpus can also be useful for unsupervised methods to select speaker for text to speech @xcite . the qasr is publicly available for the research community.