SOTAVerified

Speaker Diarization

Speaker Diarization is the task of segmenting and co-indexing audio recordings by speaker. The way the task is commonly defined, the goal is not to identify known speakers, but to co-index segments that are attributed to the same speaker; in other words, diarization implies finding speaker boundaries and grouping segments that belong to the same speaker, and, as a by-product, determining the number of distinct speakers. In combination with speech recognition, diarization enables speaker-attributed speech-to-text transcription.

Source: Improving Diarization Robustness using Diversification, Randomization and the DOVER Algorithm

Papers

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Showing 251–300 of 328 papers

TitleStatusHype
Simultaneous Speech Extraction for Multiple Target Speakers under the Meeting Scenarios—0
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models—0
Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting—0
Spatial-Temporal Activity-Informed Diarization and Separation—0
Speaker-conversation factorial designs for diarization error analysis—0
Speaker Diarization and Identification from Single-Channel Classroom Audio Recording Using Virtual Microphones—0
Speaker conditioned acoustic modeling for multi-speaker conversational ASR—0
Speaker Diarization for Low-Resource Languages Through Wav2vec Fine-Tuning—0
Speaker Diarization of Scripted Audiovisual Content—0
Speaker Diarization using Deep Recurrent Convolutional Neural Networks for Speaker Embeddings—0
Speaker diarization using latent space clustering in generative adversarial network—0
Utterance Clustering Using Stereo Audio Channels—0
Speaker Diarization With Lexical Information—0
Speaker Diarization with Lexical Information—0
Speaker diarization with session-level speaker embedding refinement using graph neural networks—0
Speaker Embeddings With Weakly Supervised Voice Activity Detection For Efficient Speaker Diarization—0
Speaker Mask Transformer for Multi-talker Overlapped Speech Recognition—0
Speaker Recognition Based on Deep Learning: An Overview—0
Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization—0
Speaker Tagging Correction With Non-Autoregressive Language Models—0
Speech Trax: A Bottom to the Top Approach for Speaker Tracking and Indexing in an Archiving Context—0
Summary of the DISPLACE Challenge 2023 -- DIarization of SPeaker and LAnguage in Conversational Environments—0
Systematic Evaluation of Online Speaker Diarization Systems Regarding their Latency—0
System Description for the Displace Speaker Diarization Challenge 2023—0
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system—0
TalTech-IRIT-LIS Speaker and Language Diarization Systems for DISPLACE 2024—0
Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario—0
Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction—0
Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker—0
Target Speaker Voice Activity Detection with Transformers and Its Integration with End-to-End Neural Diarization—0
Target Speech Diarization with Multimodal Prompts—0
TCG CREST System Description for the Second DISPLACE Challenge—0
The BabyView dataset: High-resolution egocentric videos of infants' and young children's everyday experiences—0
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System—0
The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge—0
The DKU-DukeECE-Lenovo System for the Diarization Task of the 2021 VoxCeleb Speaker Recognition Challenge—0
The ETAPE speech processing evaluation—0
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation—0
The Multimodal Information Based Speech Processing (MISP) 2025 Challenge: Audio-Visual Diarization and Recognition—0
The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge 2022 System Description—0
End-to-End Supervised Hierarchical Graph Clustering for Speaker DiarizationCode0
End-to-End Neural Speaker Diarization with Permutation-Free ObjectivesCode0
Powerset multi-class cross entropy loss for neural speaker diarizationCode0
EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of SpeakersCode0
CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning Speaker Count EstimationCode0
DiaCorrect: End-to-end error correction for speaker diarizationCode0
Probabilistic embeddings for speaker diarizationCode0
Neural Diarization with Non-autoregressive Intermediate AttractorsCode0
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial DomainCode0
On the calibration of powerset speaker diarization modelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1COS+NJW-SC (Oracle SAD)DER(%)24.05—Unverified
2EENDDER(%)23.07—Unverified
3COS+AHC (Oracle SAD)DER(%)21.13—Unverified
4SA-EEND (2-spk, no-adapt)DER(%)12.66—Unverified
5EEND-OLADER(%)12.57—Unverified
6SA-EEND (2-spk, adapted)DER(%)10.76—Unverified
7TOLDDER(%)10.14—Unverified
8COS+B-SC (Oracle SAD)DER(ig olp)8.78—Unverified
9PLDA+AHC (Oracle SAD)DER(ig olp)8.39—Unverified
10COS+NME-SC (Oracle SAD)DER(ig olp)7.29—Unverified
#ModelMetricClaimedVerifiedStatus
1x-vector (PLDA + AHC)DER(%)8.39—Unverified
2TitaNet-L (NME-SC)DER(%)6.73—Unverified
3TitaNet-M (NME-SC)DER(%)6.47—Unverified
4TitaNet-S (NME-SC)DER(%)6.37—Unverified
5x-vector (MCGAN)DER(%)5.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ECAPA (SC)DER(%)2.36—Unverified
2TitaNet-L (NME-SC)DER(%)2.03—Unverified
3TitaNet-S (NME-SC)DER(%)2—Unverified
4TitaNet-M (NME-SC)DER(%)1.99—Unverified
#ModelMetricClaimedVerifiedStatus
1TitaNet-S (NME-SC)DER(%)2.22—Unverified
2TitaNet-M (NME-SC)DER(%)1.79—Unverified
3ECAPA (SC)DER(%)1.78—Unverified
4TitaNet-L (NME-SC)DER(%)1.73—Unverified
#ModelMetricClaimedVerifiedStatus
1x-vector (PLDA + AHC)DER(%)9.72—Unverified
2TitaNet-L (NME-SC)DER(%)1.19—Unverified
3TitaNet-M (NME-SC)DER(%)1.13—Unverified
4TitaNet-S (NME-SC)DER(%)1.11—Unverified
#ModelMetricClaimedVerifiedStatus
1Baseline (the best result in the literature as of Oct.2019)DER(%)11.2—Unverified
2pyannote (MFCC)DER(%)10.5—Unverified
3pyannote (waveform)DER(%)9.9—Unverified
#ModelMetricClaimedVerifiedStatus
1BaselineDER(%)7.7—Unverified
2pyannote (MFCC)DER(%)5.6—Unverified
3pyannote (waveform)DER(%)4.9—Unverified
#ModelMetricClaimedVerifiedStatus
1pyannote (MFCC)DER(%)6.3—Unverified
2pyannote (waveform)DER(%)6—Unverified
#ModelMetricClaimedVerifiedStatus
1d-vector + spectralDER(%)12.54—Unverified
2titanet-sDER(%)1.11—Unverified
#ModelMetricClaimedVerifiedStatus
1SONDDER(%)4.46—Unverified
#ModelMetricClaimedVerifiedStatus
1UIS-RNN-SMLDER(%)27.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UIS-RNNV10.6—Unverified