# I-MSV 2022: Indic-Multilingual and Multi-sensor Speaker Verification Challenge

## TL;DR

The I-MSV 2022 Challenge focused on advancing multilingual speaker verification in India by providing a diverse dataset and benchmarking techniques robust to language and sensor variations, achieving low error rates.

## Contribution

This paper introduces the first large-scale multilingual speaker verification challenge in India, with a new dataset and evaluation framework for robust SV systems.

## Key findings

- Best constrained system achieved 2.12% EER
- Best unconstrained system achieved 0.26% EER
- Dataset includes 100 hours of speech from 100 speakers in 13 languages

## Abstract

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.