DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments

TL;DR

The DISPLACE challenge introduces a benchmark for speaker and language diarization in multilingual, code-mixed conversational speech, addressing a gap in current speech technology for complex social interactions.

Contribution

It presents the first benchmark dataset and evaluation framework for simultaneous speaker and language diarization in multilingual, code-mixed conversations.

Findings

Baseline system provided for comparison.

System submissions highlight current challenges in diarization.

Evaluation results identify key areas for future research.

Abstract

In multilingual societies, social conversations often involve code-mixed speech. The current speech technology may not be well equipped to extract information from multi-lingual multi-speaker conversations. The DISPLACE challenge entails a first-of-kind task to benchmark speaker and language diarization on the same data, as the data contains multi-speaker conversations in multilingual code-mixed speech. The challenge attempts to highlight outstanding issues in speaker diarization (SD) in multilingual settings with code-mixing. Further, language diarization (LD) in multi-speaker settings also introduces new challenges, where the system has to disambiguate speaker switches with code switches. For this challenge, a natural multilingual, multi-speaker conversational dataset is distributed for development and evaluation purposes. The systems are evaluated on single-channel far-field recordings. We also release a baseline system and report the highlights of the system submissions.