ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech
  Corpus
Injy Hamed Fadhl Eryani David Palfreyman Nizar Habash
We present ZAEBUC-Spoken, a multilingual multidialectal Arabic-English speech
corpus. The corpus comprises twelve hours of Zoom meetings involving multiple
speakers role-playing a work situation where Students brainstorm ideas for a
certain topic and then discuss it with an Interlocutor. The meetings cover
different topics and are divided into phases with different language setups.
The corpus presents a challenging set for automatic speech recognition (ASR),
including two languages (Arabic and English) with Arabic spoken in multiple
variants (Modern Standard Arabic, Gulf Arabic, and Egyptian Arabic) and English
used with various accents. Adding to the complexity of the corpus, there is
also code-switching between these languages and dialects. As part of our work,
we take inspiration from established sets of transcription guidelines to
present a set of guidelines handling issues of conversational speech,
code-switching and orthography of both languages. We further enrich the corpus
with two layers of annotations; (1) dialectness level annotation for the
portion of the corpus where mixing occurs between different variants of Arabic,
and (2) automatic morphological annotations, including tokenization,
lemmatization, and part-of-speech tagging.