mlsanityData to 14 Oct 2026

TCG CREST System Description for the DISPLACE-M Challenge

arXiv 2603.02030 ↗First posted 2 Mar 2026eess.AS cs.LGcited by 0

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Abstract (arXiv)

This report presents the TCG CREST system description for Track 1 (Speaker Diarization) of the DISPLACE-M challenge, focusing on naturalistic medical conversations in noisy rural-healthcare scenarios. Our study evaluates the impact of various voice activity detection (VAD) methods and advanced clustering algorithms on overall speaker diarization (SD) performance. We compare and analyze two SD frameworks: a modular pipeline utilizing SpeechBrain with ECAPA-TDNN embeddings, and a state-of-the-art (SOTA) hybrid end-to-end neural diarization system, Diarizen, built on top of a pre-trained WavLM. With these frameworks, we explore diverse clustering techniques, including agglomerative hierarchical clustering (AHC), and multiple novel variants of spectral clustering, such as SC-adapt, SC-PNA, and SC-MK. Experimental results demonstrate that the Diarizen system provides an approximate 39% relative improvement in the diarization error rate (DER) on the post-evaluation analysis of Phase~I compared to the SpeechBrain baseline. Our best-performing submitted system employing the Diarizen baseline with AHC employing a median filtering with a larger context window of 29 achieved a DER of 10.37% on the development and 9.21% on the evaluation sets, respectively. Our team ranked fifth out of the 11 participating teams after the Phase~I evaluation.

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References · 20

  1. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  2. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  3. 2018VoxCeleb2: Deep Speaker Recognitioncited by 875
  4. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
  5. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 406
  6. 2019pyannote.audio: neural building blocks for speaker diarizationcited by 181
  7. 2021End-to-end speaker segmentation for overlap-aware resegmentationcited by 80
  8. 2020Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard taskscited by 73
  9. 2024Open-Source Conversational AI with SpeechBrain 1.0cited by 56
  10. 2020Integrating end-to-end neural and clustering-based diarization: Getting the best of both worldscited by 35
  11. 2021Advances in integration of end-to-end neural and clustering-based diarization for real conversational speechcited by 23
  12. 2024Leveraging Self-Supervised Learning for Speaker Diarizationcited by 23
8 more
  1. 2025Fine-tune Before Structured Pruning: Towards Compact and Accurate Self-Supervised Models for Speaker Diarizationcited by 10
  2. 2025Efficient and Robust Speaker Diarization via Structured Pruning of Self-Supervised Modelscited by 7
  3. 2026Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometricscited by 6
  4. 2022Tight integration of neural- and clustering-based diarization through deep unfolding of infinite Gaussian mixture modelcited by 3
  5. 2024Self-Tuning Spectral Clustering for Speaker Diarizationcited by 3
  6. 2024Assessing the Robustness of Spectral Clustering for Deep Speaker Diarizationcited by 2
  7. 2025The TCG CREST -- RKMVERI Submission for the NCIIPC Startup India AI Grand Challengecited by 1
  8. 2026MK-SGC-SC: Multiple Kernel Guided Sparse Graph Construction in Spectral Clustering for Unsupervised Speaker Diarizationcited by 1

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