  Speaker Verification (SV) is a task to verify the claimed identity of the
claimant using his/her voice sample. Though there exists an ample amount of
research in SV technologies, the development concerning a multilingual
conversation is limited. In a country like India, almost all the speakers are
polyglot in nature. Consequently, the development of a Multilingual SV (MSV)
system on the data collected in the Indian scenario is more challenging. With
this motivation, the Indic- Multilingual Speaker Verification (I-MSV) Challenge
2022 has been designed for understanding and comparing the state-of-the-art SV
techniques. For the challenge, approximately $100$ hours of data spoken by
$100$ speakers has been collected using $5$ different sensors in $13$ Indian
languages. The data is divided into development, training, and testing sets and
has been made publicly available for further research. The goal of this
challenge is to make the SV system robust to language and sensor variations
between enrollment and testing. In the challenge, participants were asked to
develop the SV system in two scenarios, viz. constrained and unconstrained. The
best system in the constrained and unconstrained scenario achieved a
performance of $2.12\%$ and $0.26\%$ in terms of Equal Error Rate (EER),
respectively.
