  Recently, there is increasing interest in multilingual automatic speech
recognition (ASR) where a speech recognition system caters to multiple low
resource languages by taking advantage of low amounts of labeled corpora in
multiple languages. With multilingualism becoming common in today's world,
there has been increasing interest in code-switching ASR as well. In
code-switching, multiple languages are freely interchanged within a single
sentence or between sentences. The success of low-resource multilingual and
code-switching ASR often depends on the variety of languages in terms of their
acoustics, linguistic characteristics as well as the amount of data available
and how these are carefully considered in building the ASR system. In this
challenge, we would like to focus on building multilingual and code-switching
ASR systems through two different subtasks related to a total of seven Indian
languages, namely Hindi, Marathi, Odia, Tamil, Telugu, Gujarati and Bengali.
For this purpose, we provide a total of ~600 hours of transcribed speech data,
comprising train and test sets, in these languages including two code-switched
language pairs, Hindi-English and Bengali-English. We also provide a baseline
recipe for both the tasks with a WER of 30.73% and 32.45% on the test sets of
multilingual and code-switching subtasks, respectively.
