Powerset multi-class cross entropy loss for neural speaker diarization
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Abstract (arXiv)
Since its introduction in 2019, the whole end-to-end neural diarization (EEND) line of work has been addressing speaker diarization as a frame-wise multi-label classification problem with permutation-invariant training. Despite EEND showing great promise, a few recent works took a step back and studied the possible combination of (local) supervised EEND diarization with (global) unsupervised clustering. Yet, these hybrid contributions did not question the original multi-label formulation. We propose to switch from multi-label (where any two speakers can be active at the same time) to powerset multi-class classification (where dedicated classes are assigned to pairs of overlapping speakers). Through extensive experiments on 9 different benchmarks, we show that this formulation leads to significantly better performance (mostly on overlapping speech) and robustness to domain mismatch, while eliminating the detection threshold hyperparameter, critical for the multi-label formulation.
Key facts. Cited by 111 papers in the map (data through 14 Oct 2026). Builds on 19 of its 25 references that are in the map. First posted on arXiv 19 Oct 2023.
Builds on
19 of 25 references.
References · 19
- 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,556
- 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
- 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 406
- 2019pyannote.audio: neural building blocks for speaker diarizationcited by 181
- 2018Speaker Recognition from Raw Waveform with SincNetcited by 166
- 2019End-to-End Neural Speaker Diarization with Permutation-Free Objectivescited by 88
- 2021End-to-end speaker segmentation for overlap-aware resegmentationcited by 80
- 2020Spot the conversation: speaker diarisation in the wildcited by 79
- 2019End-to-End Neural Speaker Diarization with Self-attentioncited by 77
- 2020Bayesian HMM clustering of x-vector sequences (VBx) in speaker diarization: theory, implementation and analysis on standard taskscited by 73
- 2020End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractorscited by 71
- 2021M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challengecited by 71
7 more
- 2021AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenariocited by 60
- 2020Integrating end-to-end neural and clustering-based diarization: Getting the best of both worldscited by 35
- 2019Overlap-aware diarization: resegmentation using neural end-to-end overlapped speech detectioncited by 27
- 2021Advances in integration of end-to-end neural and clustering-based diarization for real conversational speechcited by 23
- 2022From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarizationcited by 20
- 2022GPU-accelerated Guided Source Separation for Meeting Transcriptioncited by 10
- 2022Speaker Overlap-aware Neural Diarization for Multi-party Meeting Analysiscited by 7
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Citing papers per year: 2023: 2; 2024: 34; 2025: 33; 2026: 42.
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- 2026Mapping Written Words to Spoken Words in a Different Language Using Only Visual Grounding27 Aug 2026
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