# Summary of the DISPLACE Challenge 2023 - DIarization of SPeaker and LAnguage in Conversational Environments

arXiv: https://arxiv.org/abs/2311.12564 · HTML: https://mlsanity.com/paper/2311.12564/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 68 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [VoxLingua107: a Dataset for Spoken Language Recognition](https://mlsanity.com/paper/2011.12998/) (2020)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [Continuous speech separation: dataset and analysis](https://mlsanity.com/paper/2001.11482/) (2020)
- [Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard tasks](https://mlsanity.com/paper/2012.14952/) (2020)
- [TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context](https://mlsanity.com/paper/2110.04410/) (2021)
- [Pushing the limits of raw waveform speaker recognition](https://mlsanity.com/paper/2203.08488/) (2022)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [ECAPA-TDNN Embeddings for Speaker Diarization](https://mlsanity.com/paper/2104.01466/) (2021)
- [The Second DIHARD Diarization Challenge: Dataset, task, and baselines](https://mlsanity.com/paper/1906.07839/) (2019)
- [Reducing Language confusion for Code-switching Speech Recognition with Token-level Language Diarization](https://mlsanity.com/paper/2210.14567/) (2022)
- [VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2012.06867/) (2020)
- [LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/1907.10393/) (2019)
- [MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection](https://mlsanity.com/paper/2010.13886/) (2020)
- [DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments](https://mlsanity.com/paper/2303.00830/) (2023)
- [OC16-CE80: A Chinese-English Mixlingual Database and A Speech Recognition Baseline](https://mlsanity.com/paper/1609.08412/) (2016)

## Built on by (most cited first, 4 of 4)

- [The Second DISPLACE Challenge : DIarization of SPeaker and LAnguage in Conversational Environments](https://mlsanity.com/paper/2406.09494/) (2024)
- [End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization](https://mlsanity.com/paper/2401.12850/) (2024)
- [ABHINAYA -- A System for Speech Emotion Recognition In Naturalistic Conditions Challenge](https://mlsanity.com/paper/2505.18217/) (2025)
- [TCG CREST System Description for the Second DISPLACE Challenge](https://mlsanity.com/paper/2409.15356/) (2024)

## Abstract (arXiv)

In multi-lingual societies, where multiple languages are spoken in a small geographic vicinity, informal conversations often involve mix of languages. Existing speech technologies may be inefficient in extracting information from such conversations, where the speech data is rich in diversity with multiple languages and speakers. The DISPLACE (DIarization of SPeaker and LAnguage in Conversational Environments) challenge constitutes an open-call for evaluating and bench-marking the speaker and language diarization technologies on this challenging condition. The challenge entailed two tracks: Track-1 focused on speaker diarization (SD) in multilingual situations while, Track-2 addressed the language diarization (LD) in a multi-speaker scenario. Both the tracks were evaluated using the same underlying audio data. To facilitate this evaluation, a real-world dataset featuring multilingual, multi-speaker conversational far-field speech was recorded and distributed. Furthermore, a baseline system was made available for both SD and LD task which mimicked the state-of-art in these tasks. The challenge garnered a total of 42 world-wide registrations and received a total of 19 combined submissions for Track-1 and Track-2. This paper describes the challenge, details of the datasets, tasks, and the baseline system. Additionally, the paper provides a concise overview of the submitted systems in both tracks, with an emphasis given to the top performing systems. The paper also presents insights and future perspectives for SD and LD tasks, focusing on the key challenges that the systems need to overcome before wide-spread commercial deployment on such conversations.